renaissance-movie-lens_0
[2024-09-25T23:06:15.962Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T23:06:15.962Z] ===============================================
[2024-09-25T23:06:15.962Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 23:06:15 2024 Epoch Time (ms): 1727305575025
[2024-09-25T23:06:15.962Z] variation: NoOptions
[2024-09-25T23:06:15.962Z] JVM_OPTIONS:
[2024-09-25T23:06:15.962Z] { \
[2024-09-25T23:06:15.962Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T23:06:15.962Z] echo "Nothing to be done for setup."; \
[2024-09-25T23:06:15.962Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17273047013474/renaissance-movie-lens_0"; \
[2024-09-25T23:06:15.962Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17273047013474/renaissance-movie-lens_0"; \
[2024-09-25T23:06:15.962Z] echo ""; echo "TESTING:"; \
[2024-09-25T23:06:15.962Z] "/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_17273047013474/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T23:06:15.963Z] 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_17273047013474/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T23:06:15.963Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T23:06:15.963Z] echo "Nothing to be done for teardown."; \
[2024-09-25T23:06:15.963Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17273047013474/TestTargetResult";
[2024-09-25T23:06:15.963Z]
[2024-09-25T23:06:15.963Z] TEST SETUP:
[2024-09-25T23:06:15.963Z] Nothing to be done for setup.
[2024-09-25T23:06:15.963Z]
[2024-09-25T23:06:15.963Z] TESTING:
[2024-09-25T23:06:18.914Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T23:06:19.843Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-25T23:06:22.799Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T23:06:22.799Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T23:06:22.799Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T23:06:23.731Z] GC before operation: completed in 52.983 ms, heap usage 80.935 MB -> 37.187 MB.
[2024-09-25T23:06:28.990Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:06:33.055Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:06:34.968Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:06:37.907Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:06:39.818Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:06:40.748Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:06:42.669Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:06:43.769Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:06:44.704Z] 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.
[2024-09-25T23:06:44.704Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:06:44.704Z] Movies recommended for you:
[2024-09-25T23:06:44.704Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:06:44.704Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:06:44.704Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21355.033 ms) ======
[2024-09-25T23:06:44.704Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T23:06:44.704Z] GC before operation: completed in 82.776 ms, heap usage 171.501 MB -> 54.340 MB.
[2024-09-25T23:06:47.666Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:06:49.595Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:06:52.547Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:06:54.460Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:06:55.392Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:06:57.305Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:06:58.234Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:07:00.180Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:07:00.180Z] 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.
[2024-09-25T23:07:00.180Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:07:00.180Z] Movies recommended for you:
[2024-09-25T23:07:00.180Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:07:00.180Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:07:00.180Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15393.804 ms) ======
[2024-09-25T23:07:00.180Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T23:07:00.180Z] GC before operation: completed in 68.964 ms, heap usage 204.437 MB -> 49.835 MB.
[2024-09-25T23:07:02.093Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:07:05.046Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:07:07.043Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:07:08.951Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:07:09.883Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:07:11.796Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:07:12.727Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:07:14.740Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:07:14.740Z] 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.
[2024-09-25T23:07:14.740Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:07:14.740Z] Movies recommended for you:
[2024-09-25T23:07:14.740Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:07:14.740Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:07:14.740Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14490.686 ms) ======
[2024-09-25T23:07:14.740Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T23:07:14.740Z] GC before operation: completed in 65.444 ms, heap usage 131.035 MB -> 49.967 MB.
[2024-09-25T23:07:16.654Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:07:18.571Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:07:21.528Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:07:23.454Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:07:24.387Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:07:26.300Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:07:27.230Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:07:29.143Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:07:29.143Z] 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.
[2024-09-25T23:07:29.143Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:07:29.143Z] Movies recommended for you:
[2024-09-25T23:07:29.143Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:07:29.143Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:07:29.143Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14405.259 ms) ======
[2024-09-25T23:07:29.143Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T23:07:29.143Z] GC before operation: completed in 68.799 ms, heap usage 122.805 MB -> 50.428 MB.
[2024-09-25T23:07:31.058Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:07:32.972Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:07:35.923Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:07:38.754Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:07:38.754Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:07:40.676Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:07:41.607Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:07:42.538Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:07:43.470Z] 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.
[2024-09-25T23:07:43.470Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:07:43.470Z] Movies recommended for you:
[2024-09-25T23:07:43.470Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:07:43.470Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:07:43.470Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14107.803 ms) ======
[2024-09-25T23:07:43.470Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T23:07:43.470Z] GC before operation: completed in 104.610 ms, heap usage 87.672 MB -> 50.557 MB.
[2024-09-25T23:07:45.391Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:07:47.303Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:07:49.215Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:07:51.130Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:07:53.041Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:07:53.972Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:07:54.901Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:07:55.832Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:07:56.762Z] 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.
[2024-09-25T23:07:56.762Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:07:56.762Z] Movies recommended for you:
[2024-09-25T23:07:56.762Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:07:56.762Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:07:56.762Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13235.355 ms) ======
[2024-09-25T23:07:56.762Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T23:07:56.762Z] GC before operation: completed in 69.674 ms, heap usage 316.890 MB -> 53.057 MB.
[2024-09-25T23:07:58.720Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:08:00.632Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:08:02.543Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:08:04.453Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:08:06.363Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:08:07.295Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:08:08.228Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:08:10.141Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:08:10.141Z] 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.
[2024-09-25T23:08:10.141Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:08:10.141Z] Movies recommended for you:
[2024-09-25T23:08:10.141Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:08:10.141Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:08:10.141Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13284.079 ms) ======
[2024-09-25T23:08:10.141Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T23:08:10.141Z] GC before operation: completed in 68.685 ms, heap usage 369.179 MB -> 50.929 MB.
[2024-09-25T23:08:12.062Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:08:14.095Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:08:16.004Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:08:17.915Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:08:18.859Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:08:20.769Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:08:21.702Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:08:22.633Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:08:23.564Z] 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.
[2024-09-25T23:08:23.564Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:08:23.564Z] Movies recommended for you:
[2024-09-25T23:08:23.564Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:08:23.564Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:08:23.564Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13184.234 ms) ======
[2024-09-25T23:08:23.564Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T23:08:23.564Z] GC before operation: completed in 70.867 ms, heap usage 705.069 MB -> 57.259 MB.
[2024-09-25T23:08:25.476Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:08:27.387Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:08:29.300Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:08:31.214Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:08:32.144Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:08:34.058Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:08:34.986Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:08:35.919Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:08:35.919Z] 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.
[2024-09-25T23:08:35.919Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:08:35.919Z] Movies recommended for you:
[2024-09-25T23:08:35.919Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:08:35.919Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:08:35.919Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12958.841 ms) ======
[2024-09-25T23:08:35.919Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T23:08:36.856Z] GC before operation: completed in 70.391 ms, heap usage 332.910 MB -> 53.230 MB.
[2024-09-25T23:08:39.473Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:08:40.405Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:08:42.322Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:08:44.236Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:08:45.165Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:08:47.075Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:08:48.011Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:08:48.941Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:08:49.872Z] 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.
[2024-09-25T23:08:49.872Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:08:49.872Z] Movies recommended for you:
[2024-09-25T23:08:49.872Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:08:49.872Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:08:49.872Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13294.368 ms) ======
[2024-09-25T23:08:49.872Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T23:08:49.872Z] GC before operation: completed in 77.709 ms, heap usage 252.628 MB -> 51.035 MB.
[2024-09-25T23:08:51.782Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:08:53.695Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:08:56.665Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:08:58.579Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:08:59.511Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:09:00.465Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:09:02.373Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:09:03.305Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:09:03.305Z] 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.
[2024-09-25T23:09:03.305Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:09:04.236Z] Movies recommended for you:
[2024-09-25T23:09:04.236Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:09:04.236Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:09:04.236Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14073.139 ms) ======
[2024-09-25T23:09:04.237Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T23:09:04.237Z] GC before operation: completed in 89.064 ms, heap usage 111.328 MB -> 53.899 MB.
[2024-09-25T23:09:06.150Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:09:08.061Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:09:09.972Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:09:11.884Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:09:13.927Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:09:14.856Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:09:15.786Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:09:16.716Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:09:17.647Z] 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.
[2024-09-25T23:09:17.647Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:09:17.647Z] Movies recommended for you:
[2024-09-25T23:09:17.647Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:09:17.647Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:09:17.647Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13545.304 ms) ======
[2024-09-25T23:09:17.647Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T23:09:17.647Z] GC before operation: completed in 81.253 ms, heap usage 402.951 MB -> 53.340 MB.
[2024-09-25T23:09:19.560Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:09:21.481Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:09:23.392Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:09:25.302Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:09:27.210Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:09:28.140Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:09:30.053Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:09:30.987Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:09:30.987Z] 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.
[2024-09-25T23:09:30.987Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:09:30.987Z] Movies recommended for you:
[2024-09-25T23:09:30.987Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:09:30.987Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:09:30.987Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13702.746 ms) ======
[2024-09-25T23:09:30.987Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T23:09:30.987Z] GC before operation: completed in 72.434 ms, heap usage 146.165 MB -> 54.520 MB.
[2024-09-25T23:09:32.897Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:09:34.811Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:09:36.726Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:09:38.638Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:09:40.327Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:09:41.256Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:09:42.189Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:09:44.102Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:09:44.102Z] 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.
[2024-09-25T23:09:44.102Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:09:44.102Z] Movies recommended for you:
[2024-09-25T23:09:44.102Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:09:44.102Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:09:44.102Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12692.446 ms) ======
[2024-09-25T23:09:44.102Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T23:09:44.102Z] GC before operation: completed in 73.296 ms, heap usage 515.276 MB -> 56.465 MB.
[2024-09-25T23:09:46.011Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:09:47.924Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:09:49.837Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:09:51.750Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:09:52.681Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:09:54.595Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:09:55.527Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:09:56.457Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:09:56.457Z] 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.
[2024-09-25T23:09:56.457Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:09:57.387Z] Movies recommended for you:
[2024-09-25T23:09:57.387Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:09:57.387Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:09:57.387Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12897.488 ms) ======
[2024-09-25T23:09:57.387Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T23:09:57.387Z] GC before operation: completed in 66.692 ms, heap usage 321.647 MB -> 53.353 MB.
[2024-09-25T23:09:59.299Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:10:01.211Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:10:03.130Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:10:05.041Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:10:05.975Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:10:06.905Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:10:08.827Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:10:09.757Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:10:09.757Z] 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.
[2024-09-25T23:10:09.757Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:10:09.757Z] Movies recommended for you:
[2024-09-25T23:10:09.757Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:10:09.757Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:10:09.757Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13159.140 ms) ======
[2024-09-25T23:10:09.757Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T23:10:10.688Z] GC before operation: completed in 72.460 ms, heap usage 250.560 MB -> 55.680 MB.
[2024-09-25T23:10:12.596Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:10:14.505Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:10:16.415Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:10:18.327Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:10:19.258Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:10:21.172Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:10:22.104Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:10:23.035Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:10:23.035Z] 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.
[2024-09-25T23:10:23.035Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:10:23.967Z] Movies recommended for you:
[2024-09-25T23:10:23.967Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:10:23.967Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:10:23.967Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13274.372 ms) ======
[2024-09-25T23:10:23.967Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T23:10:23.967Z] GC before operation: completed in 70.349 ms, heap usage 253.925 MB -> 54.202 MB.
[2024-09-25T23:10:25.880Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:10:27.791Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:10:29.703Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:10:31.614Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:10:32.547Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:10:34.470Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:10:35.403Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:10:36.336Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:10:36.336Z] 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.
[2024-09-25T23:10:36.336Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:10:37.267Z] Movies recommended for you:
[2024-09-25T23:10:37.267Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:10:37.267Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:10:37.267Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13188.761 ms) ======
[2024-09-25T23:10:37.267Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T23:10:37.267Z] GC before operation: completed in 79.555 ms, heap usage 281.811 MB -> 53.377 MB.
[2024-09-25T23:10:39.183Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:10:40.877Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:10:42.793Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:10:44.713Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:10:45.647Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:10:46.578Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:10:48.494Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:10:49.427Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:10:49.427Z] 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.
[2024-09-25T23:10:49.427Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:10:49.427Z] Movies recommended for you:
[2024-09-25T23:10:49.427Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:10:49.427Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:10:49.427Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12860.862 ms) ======
[2024-09-25T23:10:49.427Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T23:10:49.427Z] GC before operation: completed in 68.147 ms, heap usage 80.516 MB -> 51.049 MB.
[2024-09-25T23:10:51.340Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T23:10:53.254Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T23:10:55.174Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T23:10:58.126Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T23:10:59.059Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T23:10:59.990Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T23:11:01.910Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T23:11:02.845Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T23:11:02.845Z] 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.
[2024-09-25T23:11:02.845Z] The best model improves the baseline by 14.52%.
[2024-09-25T23:11:02.845Z] Movies recommended for you:
[2024-09-25T23:11:02.845Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T23:11:02.845Z] There is no way to check that no silent failure occurred.
[2024-09-25T23:11:02.845Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13267.208 ms) ======
[2024-09-25T23:11:03.778Z] -----------------------------------
[2024-09-25T23:11:03.778Z] renaissance-movie-lens_0_PASSED
[2024-09-25T23:11:03.778Z] -----------------------------------
[2024-09-25T23:11:03.778Z]
[2024-09-25T23:11:03.778Z] TEST TEARDOWN:
[2024-09-25T23:11:03.778Z] Nothing to be done for teardown.
[2024-09-25T23:11:03.778Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 23:11:03 2024 Epoch Time (ms): 1727305863106