renaissance-movie-lens_0
[2025-01-22T04:29:50.620Z] Running test renaissance-movie-lens_0 ...
[2025-01-22T04:29:50.620Z] ===============================================
[2025-01-22T04:29:50.620Z] renaissance-movie-lens_0 Start Time: Wed Jan 22 04:29:49 2025 Epoch Time (ms): 1737520189501
[2025-01-22T04:29:50.620Z] variation: NoOptions
[2025-01-22T04:29:50.620Z] JVM_OPTIONS:
[2025-01-22T04:29:50.620Z] { \
[2025-01-22T04:29:50.620Z] echo ""; echo "TEST SETUP:"; \
[2025-01-22T04:29:50.620Z] echo "Nothing to be done for setup."; \
[2025-01-22T04:29:50.620Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375192499420/renaissance-movie-lens_0"; \
[2025-01-22T04:29:50.620Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375192499420/renaissance-movie-lens_0"; \
[2025-01-22T04:29:50.620Z] echo ""; echo "TESTING:"; \
[2025-01-22T04:29:50.620Z] "/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_17375192499420/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-22T04:29:50.620Z] 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_17375192499420/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-22T04:29:50.620Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-22T04:29:50.620Z] echo "Nothing to be done for teardown."; \
[2025-01-22T04:29:50.620Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17375192499420/TestTargetResult";
[2025-01-22T04:29:50.620Z]
[2025-01-22T04:29:50.620Z] TEST SETUP:
[2025-01-22T04:29:50.620Z] Nothing to be done for setup.
[2025-01-22T04:29:50.620Z]
[2025-01-22T04:29:50.620Z] TESTING:
[2025-01-22T04:29:53.633Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-22T04:29:55.609Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-01-22T04:29:58.620Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-22T04:29:58.620Z] Training: 60056, validation: 20285, test: 19854
[2025-01-22T04:29:58.620Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-22T04:29:58.620Z] GC before operation: completed in 62.272 ms, heap usage 46.670 MB -> 36.446 MB.
[2025-01-22T04:30:05.336Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:30:08.347Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:30:11.357Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:30:14.387Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:30:15.347Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:30:17.307Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:30:19.267Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:30:20.218Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:30:21.173Z] 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.
[2025-01-22T04:30:21.173Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:30:21.173Z] Movies recommended for you:
[2025-01-22T04:30:21.173Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:30:21.173Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:30:21.173Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21984.645 ms) ======
[2025-01-22T04:30:21.173Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-22T04:30:21.173Z] GC before operation: completed in 92.999 ms, heap usage 391.919 MB -> 50.659 MB.
[2025-01-22T04:30:24.212Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:30:26.173Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:30:29.191Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:30:31.140Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:30:33.133Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:30:34.084Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:30:36.036Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:30:37.993Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:30:37.993Z] 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.
[2025-01-22T04:30:37.993Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:30:37.993Z] Movies recommended for you:
[2025-01-22T04:30:37.993Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:30:37.993Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:30:37.993Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16930.077 ms) ======
[2025-01-22T04:30:37.993Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-22T04:30:37.993Z] GC before operation: completed in 91.558 ms, heap usage 341.409 MB -> 49.253 MB.
[2025-01-22T04:30:41.005Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:30:42.958Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:30:44.911Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:30:47.924Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:30:48.874Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:30:50.848Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:30:51.801Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:30:53.757Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:30:53.757Z] 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.
[2025-01-22T04:30:53.757Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:30:53.757Z] Movies recommended for you:
[2025-01-22T04:30:53.757Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:30:53.757Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:30:53.757Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (15611.087 ms) ======
[2025-01-22T04:30:53.757Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-22T04:30:53.757Z] GC before operation: completed in 92.275 ms, heap usage 269.049 MB -> 49.410 MB.
[2025-01-22T04:30:56.770Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:30:58.723Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:31:00.673Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:31:02.629Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:31:04.577Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:31:05.529Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:31:07.482Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:31:08.433Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:31:08.434Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-01-22T04:31:08.434Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:31:08.434Z] Movies recommended for you:
[2025-01-22T04:31:08.434Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:31:08.434Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:31:08.434Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15029.400 ms) ======
[2025-01-22T04:31:08.434Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-22T04:31:08.434Z] GC before operation: completed in 90.974 ms, heap usage 207.663 MB -> 49.728 MB.
[2025-01-22T04:31:11.459Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:31:13.413Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:31:16.428Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:31:18.381Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:31:19.332Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:31:21.283Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:31:22.235Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:31:24.396Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:31:24.396Z] 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.
[2025-01-22T04:31:24.396Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:31:24.396Z] Movies recommended for you:
[2025-01-22T04:31:24.396Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:31:24.396Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:31:24.396Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15080.319 ms) ======
[2025-01-22T04:31:24.396Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-22T04:31:24.396Z] GC before operation: completed in 81.999 ms, heap usage 166.412 MB -> 49.867 MB.
[2025-01-22T04:31:26.353Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:31:28.303Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:31:30.263Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:31:32.293Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:31:34.244Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:31:35.231Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:31:36.181Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:31:38.132Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:31:38.132Z] 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.
[2025-01-22T04:31:38.132Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:31:38.132Z] Movies recommended for you:
[2025-01-22T04:31:38.132Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:31:38.132Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:31:38.132Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14141.613 ms) ======
[2025-01-22T04:31:38.132Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-22T04:31:38.132Z] GC before operation: completed in 85.744 ms, heap usage 276.550 MB -> 49.881 MB.
[2025-01-22T04:31:41.145Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:31:43.096Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:31:45.047Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:31:46.997Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:31:49.023Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:31:49.973Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:31:50.936Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:31:52.891Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:31:52.891Z] 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.
[2025-01-22T04:31:52.891Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:31:52.891Z] Movies recommended for you:
[2025-01-22T04:31:52.891Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:31:52.891Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:31:52.891Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14674.417 ms) ======
[2025-01-22T04:31:52.891Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-22T04:31:52.891Z] GC before operation: completed in 83.390 ms, heap usage 180.174 MB -> 49.943 MB.
[2025-01-22T04:31:54.845Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:31:57.857Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:31:59.807Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:32:01.760Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:32:02.710Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:32:04.662Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:32:05.614Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:32:06.568Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:32:07.524Z] 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.
[2025-01-22T04:32:07.524Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:32:07.524Z] Movies recommended for you:
[2025-01-22T04:32:07.524Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:32:07.524Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:32:07.524Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14231.577 ms) ======
[2025-01-22T04:32:07.524Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-22T04:32:07.524Z] GC before operation: completed in 90.466 ms, heap usage 104.407 MB -> 50.149 MB.
[2025-01-22T04:32:09.475Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:32:11.426Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:32:13.381Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:32:15.337Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:32:17.289Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:32:18.240Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:32:19.192Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:32:21.163Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:32:21.163Z] 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.
[2025-01-22T04:32:21.163Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:32:21.163Z] Movies recommended for you:
[2025-01-22T04:32:21.163Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:32:21.163Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:32:21.163Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13941.193 ms) ======
[2025-01-22T04:32:21.163Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-22T04:32:21.163Z] GC before operation: completed in 84.623 ms, heap usage 276.866 MB -> 50.183 MB.
[2025-01-22T04:32:24.007Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:32:26.302Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:32:28.254Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:32:30.205Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:32:31.155Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:32:33.138Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:32:34.088Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:32:35.040Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:32:36.009Z] 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.
[2025-01-22T04:32:36.009Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:32:36.009Z] Movies recommended for you:
[2025-01-22T04:32:36.009Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:32:36.009Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:32:36.009Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14433.854 ms) ======
[2025-01-22T04:32:36.009Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-22T04:32:36.009Z] GC before operation: completed in 86.154 ms, heap usage 241.507 MB -> 50.239 MB.
[2025-01-22T04:32:37.964Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:32:39.914Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:32:41.865Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:32:43.834Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:32:45.793Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:32:46.745Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:32:47.695Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:32:49.646Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:32:49.646Z] 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.
[2025-01-22T04:32:49.646Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:32:49.646Z] Movies recommended for you:
[2025-01-22T04:32:49.646Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:32:49.646Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:32:49.646Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13950.628 ms) ======
[2025-01-22T04:32:49.646Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-22T04:32:49.646Z] GC before operation: completed in 85.609 ms, heap usage 258.310 MB -> 50.014 MB.
[2025-01-22T04:32:52.657Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:32:54.639Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:32:56.589Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:32:58.556Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:33:00.509Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:33:01.459Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:33:02.419Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:33:04.373Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:33:04.373Z] 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.
[2025-01-22T04:33:04.373Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:33:04.373Z] Movies recommended for you:
[2025-01-22T04:33:04.373Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:33:04.373Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:33:04.373Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14371.325 ms) ======
[2025-01-22T04:33:04.373Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-22T04:33:04.373Z] GC before operation: completed in 80.979 ms, heap usage 329.773 MB -> 50.207 MB.
[2025-01-22T04:33:06.323Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:33:09.343Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:33:11.292Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:33:13.246Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:33:14.197Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:33:16.147Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:33:17.097Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:33:18.050Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:33:19.001Z] 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.
[2025-01-22T04:33:19.001Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:33:19.001Z] Movies recommended for you:
[2025-01-22T04:33:19.001Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:33:19.001Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:33:19.001Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14473.577 ms) ======
[2025-01-22T04:33:19.001Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-22T04:33:19.001Z] GC before operation: completed in 86.871 ms, heap usage 227.006 MB -> 50.285 MB.
[2025-01-22T04:33:20.958Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:33:22.913Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:33:25.833Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:33:27.783Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:33:28.736Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:33:29.686Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:33:31.638Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:33:32.593Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:33:32.593Z] 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.
[2025-01-22T04:33:32.593Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:33:33.544Z] Movies recommended for you:
[2025-01-22T04:33:33.544Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:33:33.544Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:33:33.544Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14183.425 ms) ======
[2025-01-22T04:33:33.544Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-22T04:33:33.544Z] GC before operation: completed in 84.138 ms, heap usage 198.518 MB -> 49.985 MB.
[2025-01-22T04:33:35.493Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:33:37.444Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:33:39.397Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:33:41.349Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:33:43.301Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:33:44.264Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:33:45.223Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:33:47.189Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:33:47.189Z] 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.
[2025-01-22T04:33:47.189Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:33:47.189Z] Movies recommended for you:
[2025-01-22T04:33:47.189Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:33:47.189Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:33:47.189Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13993.499 ms) ======
[2025-01-22T04:33:47.189Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-22T04:33:47.189Z] GC before operation: completed in 82.193 ms, heap usage 95.108 MB -> 50.100 MB.
[2025-01-22T04:33:49.158Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:33:52.174Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:33:54.133Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:33:56.085Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:33:57.034Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:33:57.987Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:33:59.939Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:34:00.890Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:34:01.841Z] 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.
[2025-01-22T04:34:01.841Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:34:01.841Z] Movies recommended for you:
[2025-01-22T04:34:01.841Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:34:01.841Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:34:01.841Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14243.319 ms) ======
[2025-01-22T04:34:01.841Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-22T04:34:01.841Z] GC before operation: completed in 83.286 ms, heap usage 154.967 MB -> 50.276 MB.
[2025-01-22T04:34:03.791Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:34:05.740Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:34:07.691Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:34:09.640Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:34:11.599Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:34:12.736Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:34:13.687Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:34:15.638Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:34:15.638Z] 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.
[2025-01-22T04:34:15.638Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:34:15.638Z] Movies recommended for you:
[2025-01-22T04:34:15.638Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:34:15.638Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:34:15.638Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13941.052 ms) ======
[2025-01-22T04:34:15.638Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-22T04:34:15.638Z] GC before operation: completed in 93.721 ms, heap usage 157.958 MB -> 50.070 MB.
[2025-01-22T04:34:17.588Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:34:19.539Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:34:23.613Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:34:24.732Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:34:25.684Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:34:26.635Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:34:27.632Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:34:29.584Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:34:29.584Z] 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.
[2025-01-22T04:34:29.584Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:34:29.584Z] Movies recommended for you:
[2025-01-22T04:34:29.584Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:34:29.584Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:34:29.584Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13849.695 ms) ======
[2025-01-22T04:34:29.584Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-22T04:34:29.584Z] GC before operation: completed in 81.317 ms, heap usage 75.145 MB -> 50.050 MB.
[2025-01-22T04:34:31.538Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:34:33.499Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:34:36.513Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:34:38.463Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:34:39.412Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:34:40.362Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:34:42.317Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:34:43.269Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:34:43.269Z] 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.
[2025-01-22T04:34:43.269Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:34:43.270Z] Movies recommended for you:
[2025-01-22T04:34:43.270Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:34:43.270Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:34:43.270Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14027.065 ms) ======
[2025-01-22T04:34:43.270Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-22T04:34:43.270Z] GC before operation: completed in 90.723 ms, heap usage 305.462 MB -> 50.450 MB.
[2025-01-22T04:34:46.304Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T04:34:48.255Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T04:34:50.207Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T04:34:52.274Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T04:34:53.225Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T04:34:55.173Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T04:34:56.123Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T04:34:57.083Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T04:34:58.033Z] 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.
[2025-01-22T04:34:58.033Z] The best model improves the baseline by 14.52%.
[2025-01-22T04:34:58.033Z] Movies recommended for you:
[2025-01-22T04:34:58.033Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T04:34:58.033Z] There is no way to check that no silent failure occurred.
[2025-01-22T04:34:58.033Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14184.847 ms) ======
[2025-01-22T04:34:58.033Z] -----------------------------------
[2025-01-22T04:34:58.033Z] renaissance-movie-lens_0_PASSED
[2025-01-22T04:34:58.033Z] -----------------------------------
[2025-01-22T04:34:58.033Z]
[2025-01-22T04:34:58.033Z] TEST TEARDOWN:
[2025-01-22T04:34:58.033Z] Nothing to be done for teardown.
[2025-01-22T04:34:58.033Z] renaissance-movie-lens_0 Finish Time: Wed Jan 22 04:34:57 2025 Epoch Time (ms): 1737520497915