renaissance-movie-lens_0
[2025-01-10T18:04:11.338Z] Running test renaissance-movie-lens_0 ...
[2025-01-10T18:04:11.338Z] ===============================================
[2025-01-10T18:04:11.338Z] renaissance-movie-lens_0 Start Time: Fri Jan 10 18:04:10 2025 Epoch Time (ms): 1736532250462
[2025-01-10T18:04:11.338Z] variation: NoOptions
[2025-01-10T18:04:11.338Z] JVM_OPTIONS:
[2025-01-10T18:04:11.338Z] { \
[2025-01-10T18:04:11.338Z] echo ""; echo "TEST SETUP:"; \
[2025-01-10T18:04:11.338Z] echo "Nothing to be done for setup."; \
[2025-01-10T18:04:11.338Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17365313806972/renaissance-movie-lens_0"; \
[2025-01-10T18:04:11.338Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17365313806972/renaissance-movie-lens_0"; \
[2025-01-10T18:04:11.338Z] echo ""; echo "TESTING:"; \
[2025-01-10T18:04:11.338Z] "/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_17365313806972/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-10T18:04:11.338Z] 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_17365313806972/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-10T18:04:11.338Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-10T18:04:11.338Z] echo "Nothing to be done for teardown."; \
[2025-01-10T18:04:11.338Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17365313806972/TestTargetResult";
[2025-01-10T18:04:11.338Z]
[2025-01-10T18:04:11.338Z] TEST SETUP:
[2025-01-10T18:04:11.338Z] Nothing to be done for setup.
[2025-01-10T18:04:11.338Z]
[2025-01-10T18:04:11.338Z] TESTING:
[2025-01-10T18:04:14.395Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-10T18:04:16.349Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-01-10T18:04:19.370Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-10T18:04:19.370Z] Training: 60056, validation: 20285, test: 19854
[2025-01-10T18:04:19.370Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-10T18:04:19.370Z] GC before operation: completed in 68.136 ms, heap usage 49.515 MB -> 36.458 MB.
[2025-01-10T18:04:26.068Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:04:29.085Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:04:32.098Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:04:34.057Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:04:36.018Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:04:36.969Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:04:38.924Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:04:40.883Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:04:40.883Z] 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.
[2025-01-10T18:04:40.883Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:04:40.883Z] Movies recommended for you:
[2025-01-10T18:04:40.883Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:04:40.883Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:04:40.883Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21268.684 ms) ======
[2025-01-10T18:04:40.883Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-10T18:04:40.883Z] GC before operation: completed in 90.660 ms, heap usage 230.859 MB -> 50.048 MB.
[2025-01-10T18:04:44.059Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:04:46.009Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:04:49.020Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:04:50.973Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:04:52.933Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:04:53.891Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:04:55.842Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:04:56.896Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:04:56.896Z] 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.
[2025-01-10T18:04:56.896Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:04:57.845Z] Movies recommended for you:
[2025-01-10T18:04:57.845Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:04:57.845Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:04:57.845Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16245.342 ms) ======
[2025-01-10T18:04:57.845Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-10T18:04:57.845Z] GC before operation: completed in 87.085 ms, heap usage 173.880 MB -> 49.030 MB.
[2025-01-10T18:04:59.798Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:05:01.768Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:05:04.791Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:05:06.745Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:05:07.694Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:05:08.645Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:05:10.596Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:05:11.546Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:05:12.510Z] 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.
[2025-01-10T18:05:12.510Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:05:12.510Z] Movies recommended for you:
[2025-01-10T18:05:12.510Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:05:12.510Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:05:12.510Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14670.048 ms) ======
[2025-01-10T18:05:12.510Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-10T18:05:12.510Z] GC before operation: completed in 88.205 ms, heap usage 113.249 MB -> 49.264 MB.
[2025-01-10T18:05:14.460Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:05:16.425Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:05:19.439Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:05:21.390Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:05:22.342Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:05:24.311Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:05:25.260Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:05:26.211Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:05:27.163Z] 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.
[2025-01-10T18:05:27.163Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:05:27.163Z] Movies recommended for you:
[2025-01-10T18:05:27.163Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:05:27.163Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:05:27.163Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14719.870 ms) ======
[2025-01-10T18:05:27.163Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-10T18:05:27.163Z] GC before operation: completed in 82.527 ms, heap usage 297.118 MB -> 49.772 MB.
[2025-01-10T18:05:29.115Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:05:32.239Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:05:34.193Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:05:36.878Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:05:37.850Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:05:38.835Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:05:39.808Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:05:41.764Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:05:41.764Z] 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.
[2025-01-10T18:05:41.764Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:05:41.764Z] Movies recommended for you:
[2025-01-10T18:05:41.764Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:05:41.764Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:05:41.764Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14747.029 ms) ======
[2025-01-10T18:05:41.764Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-10T18:05:41.764Z] GC before operation: completed in 81.522 ms, heap usage 83.874 MB -> 49.702 MB.
[2025-01-10T18:05:43.720Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:05:45.670Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:05:48.688Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:05:50.647Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:05:51.599Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:05:52.554Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:05:54.509Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:05:55.475Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:05:55.476Z] 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.
[2025-01-10T18:05:55.476Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:05:56.542Z] Movies recommended for you:
[2025-01-10T18:05:56.542Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:05:56.542Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:05:56.542Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14073.201 ms) ======
[2025-01-10T18:05:56.542Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-10T18:05:56.542Z] GC before operation: completed in 81.564 ms, heap usage 262.434 MB -> 49.886 MB.
[2025-01-10T18:05:58.498Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:06:00.453Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:06:02.406Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:06:04.356Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:06:06.313Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:06:07.266Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:06:09.226Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:06:10.179Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:06:10.179Z] 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.
[2025-01-10T18:06:10.179Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:06:10.179Z] Movies recommended for you:
[2025-01-10T18:06:10.179Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:06:10.179Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:06:10.179Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14533.075 ms) ======
[2025-01-10T18:06:10.179Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-10T18:06:11.131Z] GC before operation: completed in 90.576 ms, heap usage 320.889 MB -> 50.069 MB.
[2025-01-10T18:06:13.084Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:06:15.041Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:06:16.994Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:06:18.951Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:06:19.905Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:06:21.859Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:06:22.811Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:06:23.762Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:06:24.714Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-10T18:06:24.714Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:06:24.714Z] Movies recommended for you:
[2025-01-10T18:06:24.714Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:06:24.714Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:06:24.714Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13766.373 ms) ======
[2025-01-10T18:06:24.714Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-10T18:06:24.714Z] GC before operation: completed in 84.889 ms, heap usage 150.181 MB -> 50.199 MB.
[2025-01-10T18:06:26.667Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:06:28.623Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:06:31.626Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:06:32.590Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:06:34.547Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:06:35.499Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:06:37.454Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:06:38.407Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:06:38.407Z] 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.
[2025-01-10T18:06:38.407Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:06:38.407Z] Movies recommended for you:
[2025-01-10T18:06:38.407Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:06:38.407Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:06:38.407Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14177.058 ms) ======
[2025-01-10T18:06:38.407Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-10T18:06:38.407Z] GC before operation: completed in 82.980 ms, heap usage 196.670 MB -> 50.037 MB.
[2025-01-10T18:06:41.427Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:06:43.380Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:06:45.505Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:06:47.460Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:06:48.412Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:06:49.363Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:06:51.316Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:06:52.267Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:06:52.267Z] 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.
[2025-01-10T18:06:52.267Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:06:52.267Z] Movies recommended for you:
[2025-01-10T18:06:52.267Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:06:52.267Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:06:52.267Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13739.697 ms) ======
[2025-01-10T18:06:52.267Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-10T18:06:52.267Z] GC before operation: completed in 85.721 ms, heap usage 62.417 MB -> 50.026 MB.
[2025-01-10T18:06:55.282Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:06:57.358Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:06:59.334Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:07:00.285Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:07:02.236Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:07:03.187Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:07:04.150Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:07:06.102Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:07:06.102Z] 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.
[2025-01-10T18:07:06.102Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:07:06.102Z] Movies recommended for you:
[2025-01-10T18:07:06.102Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:07:06.102Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:07:06.102Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13599.401 ms) ======
[2025-01-10T18:07:06.102Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-10T18:07:06.102Z] GC before operation: completed in 93.625 ms, heap usage 89.391 MB -> 49.857 MB.
[2025-01-10T18:07:08.051Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:07:10.003Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:07:13.016Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:07:14.980Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:07:15.937Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:07:16.897Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:07:19.721Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:07:19.721Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:07:19.721Z] 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.
[2025-01-10T18:07:19.721Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:07:19.721Z] Movies recommended for you:
[2025-01-10T18:07:19.721Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:07:19.721Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:07:19.721Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13654.825 ms) ======
[2025-01-10T18:07:19.721Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-10T18:07:19.721Z] GC before operation: completed in 83.274 ms, heap usage 187.256 MB -> 50.067 MB.
[2025-01-10T18:07:22.747Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:07:24.697Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:07:26.647Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:07:28.599Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:07:29.549Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:07:31.501Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:07:32.453Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:07:33.404Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:07:34.355Z] 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.
[2025-01-10T18:07:34.355Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:07:34.355Z] Movies recommended for you:
[2025-01-10T18:07:34.355Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:07:34.355Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:07:34.355Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13946.129 ms) ======
[2025-01-10T18:07:34.355Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-10T18:07:34.355Z] GC before operation: completed in 81.996 ms, heap usage 319.785 MB -> 50.389 MB.
[2025-01-10T18:07:36.310Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:07:38.260Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:07:40.229Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:07:42.181Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:07:43.135Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:07:45.087Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:07:46.037Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:07:46.990Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:07:47.941Z] 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.
[2025-01-10T18:07:47.941Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:07:47.941Z] Movies recommended for you:
[2025-01-10T18:07:47.941Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:07:47.941Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:07:47.941Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13600.791 ms) ======
[2025-01-10T18:07:47.941Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-10T18:07:47.941Z] GC before operation: completed in 81.188 ms, heap usage 201.451 MB -> 49.960 MB.
[2025-01-10T18:07:49.894Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:07:51.845Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:07:53.798Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:07:55.750Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:07:57.716Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:07:58.667Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:07:59.619Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:08:01.569Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:08:01.569Z] 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.
[2025-01-10T18:08:01.569Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:08:01.569Z] Movies recommended for you:
[2025-01-10T18:08:01.570Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:08:01.570Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:08:01.570Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13730.437 ms) ======
[2025-01-10T18:08:01.570Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-10T18:08:01.570Z] GC before operation: completed in 84.759 ms, heap usage 187.463 MB -> 50.167 MB.
[2025-01-10T18:08:03.523Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:08:06.535Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:08:08.485Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:08:10.482Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:08:11.433Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:08:12.383Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:08:15.216Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:08:15.216Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:08:15.216Z] 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.
[2025-01-10T18:08:16.167Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:08:16.167Z] Movies recommended for you:
[2025-01-10T18:08:16.167Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:08:16.167Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:08:16.167Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14222.185 ms) ======
[2025-01-10T18:08:16.167Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-10T18:08:16.167Z] GC before operation: completed in 87.169 ms, heap usage 123.536 MB -> 50.180 MB.
[2025-01-10T18:08:18.120Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:08:20.074Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:08:22.033Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:08:23.994Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:08:24.945Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:08:26.896Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:08:27.850Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:08:28.804Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:08:29.756Z] 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.
[2025-01-10T18:08:29.756Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:08:29.756Z] Movies recommended for you:
[2025-01-10T18:08:29.756Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:08:29.756Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:08:29.756Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13603.524 ms) ======
[2025-01-10T18:08:29.756Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-10T18:08:29.756Z] GC before operation: completed in 88.945 ms, heap usage 59.720 MB -> 49.903 MB.
[2025-01-10T18:08:31.711Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:08:33.666Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:08:36.683Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:08:38.636Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:08:39.585Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:08:40.536Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:08:42.484Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:08:43.435Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:08:43.435Z] 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.
[2025-01-10T18:08:43.435Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:08:43.435Z] Movies recommended for you:
[2025-01-10T18:08:43.435Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:08:43.435Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:08:43.435Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14105.508 ms) ======
[2025-01-10T18:08:43.435Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-10T18:08:43.435Z] GC before operation: completed in 94.660 ms, heap usage 172.395 MB -> 50.154 MB.
[2025-01-10T18:08:46.455Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:08:48.410Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:08:50.362Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:08:52.319Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:08:53.274Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:08:55.226Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:08:56.177Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:08:57.129Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:08:58.084Z] 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.
[2025-01-10T18:08:58.084Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:08:58.084Z] Movies recommended for you:
[2025-01-10T18:08:58.084Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:08:58.084Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:08:58.084Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14144.168 ms) ======
[2025-01-10T18:08:58.084Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-10T18:08:58.084Z] GC before operation: completed in 90.722 ms, heap usage 205.691 MB -> 50.281 MB.
[2025-01-10T18:09:00.044Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:09:01.997Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:09:04.997Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:09:06.951Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:09:07.903Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:09:09.854Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:09:10.808Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:09:11.760Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:09:12.723Z] 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.
[2025-01-10T18:09:12.723Z] The best model improves the baseline by 14.52%.
[2025-01-10T18:09:12.723Z] Movies recommended for you:
[2025-01-10T18:09:12.723Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:09:12.723Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:09:12.723Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14520.771 ms) ======
[2025-01-10T18:09:12.723Z] -----------------------------------
[2025-01-10T18:09:12.723Z] renaissance-movie-lens_0_PASSED
[2025-01-10T18:09:12.723Z] -----------------------------------
[2025-01-10T18:09:12.723Z]
[2025-01-10T18:09:12.723Z] TEST TEARDOWN:
[2025-01-10T18:09:12.723Z] Nothing to be done for teardown.
[2025-01-10T18:09:12.723Z] renaissance-movie-lens_0 Finish Time: Fri Jan 10 18:09:12 2025 Epoch Time (ms): 1736532552601