renaissance-movie-lens_0
[2024-11-17T22:31:29.428Z] Running test renaissance-movie-lens_0 ...
[2024-11-17T22:31:29.428Z] ===============================================
[2024-11-17T22:31:29.428Z] renaissance-movie-lens_0 Start Time: Sun Nov 17 22:31:28 2024 Epoch Time (ms): 1731882688981
[2024-11-17T22:31:29.428Z] variation: NoOptions
[2024-11-17T22:31:29.428Z] JVM_OPTIONS:
[2024-11-17T22:31:29.428Z] { \
[2024-11-17T22:31:29.428Z] echo ""; echo "TEST SETUP:"; \
[2024-11-17T22:31:29.428Z] echo "Nothing to be done for setup."; \
[2024-11-17T22:31:29.428Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17318814988430/renaissance-movie-lens_0"; \
[2024-11-17T22:31:29.428Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17318814988430/renaissance-movie-lens_0"; \
[2024-11-17T22:31:29.428Z] echo ""; echo "TESTING:"; \
[2024-11-17T22:31:29.428Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_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_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17318814988430/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-17T22:31:29.428Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17318814988430/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-17T22:31:29.428Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-17T22:31:29.428Z] echo "Nothing to be done for teardown."; \
[2024-11-17T22:31:29.428Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17318814988430/TestTargetResult";
[2024-11-17T22:31:29.428Z]
[2024-11-17T22:31:29.428Z] TEST SETUP:
[2024-11-17T22:31:29.428Z] Nothing to be done for setup.
[2024-11-17T22:31:29.428Z]
[2024-11-17T22:31:29.428Z] TESTING:
[2024-11-17T22:31:33.462Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-17T22:31:36.708Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-17T22:31:41.865Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-17T22:31:41.865Z] Training: 60056, validation: 20285, test: 19854
[2024-11-17T22:31:41.865Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-17T22:31:41.865Z] GC before operation: completed in 88.079 ms, heap usage 102.565 MB -> 36.444 MB.
[2024-11-17T22:31:53.340Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:31:57.493Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:32:02.799Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:32:08.120Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:32:10.081Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:32:11.968Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:32:14.428Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:32:16.968Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:32:16.968Z] 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.
[2024-11-17T22:32:17.357Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:32:17.357Z] Movies recommended for you:
[2024-11-17T22:32:17.357Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:32:17.357Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:32:17.357Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (35385.465 ms) ======
[2024-11-17T22:32:17.357Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-17T22:32:17.723Z] GC before operation: completed in 124.672 ms, heap usage 198.987 MB -> 52.781 MB.
[2024-11-17T22:32:21.013Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:32:25.143Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:32:29.226Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:32:32.423Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:32:34.328Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:32:36.154Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:32:38.797Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:32:40.643Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:32:41.022Z] 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.
[2024-11-17T22:32:41.022Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:32:41.022Z] Movies recommended for you:
[2024-11-17T22:32:41.022Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:32:41.022Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:32:41.022Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (23568.416 ms) ======
[2024-11-17T22:32:41.022Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-17T22:32:41.386Z] GC before operation: completed in 106.901 ms, heap usage 105.508 MB -> 48.945 MB.
[2024-11-17T22:32:44.636Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:32:48.707Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:32:51.941Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:32:54.405Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:32:56.897Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:32:58.696Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:33:01.178Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:33:03.036Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:33:03.414Z] 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.
[2024-11-17T22:33:03.414Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:33:03.414Z] Movies recommended for you:
[2024-11-17T22:33:03.414Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:33:03.414Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:33:03.414Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (22275.104 ms) ======
[2024-11-17T22:33:03.414Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-17T22:33:03.414Z] GC before operation: completed in 93.548 ms, heap usage 152.477 MB -> 49.293 MB.
[2024-11-17T22:33:06.705Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:33:09.978Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:33:13.214Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:33:16.424Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:33:18.293Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:33:20.125Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:33:21.932Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:33:23.788Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:33:24.196Z] 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.
[2024-11-17T22:33:24.196Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:33:24.196Z] Movies recommended for you:
[2024-11-17T22:33:24.196Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:33:24.196Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:33:24.196Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20753.328 ms) ======
[2024-11-17T22:33:24.196Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-17T22:33:24.569Z] GC before operation: completed in 93.286 ms, heap usage 183.048 MB -> 49.671 MB.
[2024-11-17T22:33:27.784Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:33:31.049Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:33:34.264Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:33:37.560Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:33:39.380Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:33:41.183Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:33:42.993Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:33:45.452Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:33:45.452Z] 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.
[2024-11-17T22:33:45.452Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:33:45.823Z] Movies recommended for you:
[2024-11-17T22:33:45.823Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:33:45.823Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:33:45.823Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21231.969 ms) ======
[2024-11-17T22:33:45.823Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-17T22:33:45.823Z] GC before operation: completed in 98.982 ms, heap usage 177.012 MB -> 49.859 MB.
[2024-11-17T22:33:49.064Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:33:52.274Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:33:55.454Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:33:58.735Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:34:00.586Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:34:02.406Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:34:04.376Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:34:06.234Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:34:06.234Z] 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.
[2024-11-17T22:34:06.234Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:34:06.608Z] Movies recommended for you:
[2024-11-17T22:34:06.608Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:34:06.608Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:34:06.608Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20728.783 ms) ======
[2024-11-17T22:34:06.608Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-17T22:34:06.608Z] GC before operation: completed in 102.732 ms, heap usage 229.409 MB -> 49.804 MB.
[2024-11-17T22:34:09.783Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:34:12.999Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:34:15.456Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:34:18.678Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:34:20.515Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:34:22.336Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:34:24.836Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:34:26.156Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:34:26.542Z] 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.
[2024-11-17T22:34:26.542Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:34:26.932Z] Movies recommended for you:
[2024-11-17T22:34:26.932Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:34:26.932Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:34:26.932Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20164.927 ms) ======
[2024-11-17T22:34:26.932Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-17T22:34:26.932Z] GC before operation: completed in 95.311 ms, heap usage 172.919 MB -> 49.973 MB.
[2024-11-17T22:34:30.154Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:34:33.426Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:34:35.928Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:34:39.121Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:34:40.956Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:34:42.234Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:34:44.075Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:34:45.988Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:34:46.364Z] 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.
[2024-11-17T22:34:46.364Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:34:46.364Z] Movies recommended for you:
[2024-11-17T22:34:46.364Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:34:46.364Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:34:46.364Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19605.958 ms) ======
[2024-11-17T22:34:46.364Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-17T22:34:46.731Z] GC before operation: completed in 93.168 ms, heap usage 228.956 MB -> 50.240 MB.
[2024-11-17T22:34:49.930Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:34:52.427Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:34:55.635Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:34:58.124Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:35:00.076Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:35:01.955Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:35:03.780Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:35:05.619Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:35:05.619Z] 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.
[2024-11-17T22:35:05.619Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:35:05.986Z] Movies recommended for you:
[2024-11-17T22:35:05.986Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:35:05.986Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:35:05.986Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19256.884 ms) ======
[2024-11-17T22:35:05.986Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-17T22:35:05.986Z] GC before operation: completed in 103.307 ms, heap usage 175.851 MB -> 50.067 MB.
[2024-11-17T22:35:09.216Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:35:11.682Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:35:14.924Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:35:17.382Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:35:19.237Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:35:21.058Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:35:22.866Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:35:24.692Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:35:24.692Z] 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.
[2024-11-17T22:35:25.056Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:35:25.056Z] Movies recommended for you:
[2024-11-17T22:35:25.056Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:35:25.056Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:35:25.056Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19107.309 ms) ======
[2024-11-17T22:35:25.056Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-17T22:35:25.056Z] GC before operation: completed in 93.775 ms, heap usage 106.697 MB -> 51.264 MB.
[2024-11-17T22:35:28.292Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:35:30.898Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:35:34.104Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:35:36.579Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:35:38.396Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:35:40.213Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:35:42.070Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:35:43.998Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:35:44.367Z] 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.
[2024-11-17T22:35:44.367Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:35:44.367Z] Movies recommended for you:
[2024-11-17T22:35:44.367Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:35:44.367Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:35:44.367Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19260.855 ms) ======
[2024-11-17T22:35:44.367Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-17T22:35:44.367Z] GC before operation: completed in 93.451 ms, heap usage 142.531 MB -> 49.831 MB.
[2024-11-17T22:35:47.611Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:35:50.865Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:35:53.367Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:35:56.716Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:35:58.004Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:35:59.844Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:36:01.658Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:36:03.493Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:36:03.493Z] 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.
[2024-11-17T22:36:03.493Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:36:03.875Z] Movies recommended for you:
[2024-11-17T22:36:03.875Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:36:03.875Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:36:03.875Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19225.548 ms) ======
[2024-11-17T22:36:03.875Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-17T22:36:03.875Z] GC before operation: completed in 95.755 ms, heap usage 103.571 MB -> 50.502 MB.
[2024-11-17T22:36:07.104Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:36:10.351Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:36:12.860Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:36:16.074Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:36:17.935Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:36:19.774Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:36:21.646Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:36:24.183Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:36:24.183Z] 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.
[2024-11-17T22:36:24.183Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:36:24.556Z] Movies recommended for you:
[2024-11-17T22:36:24.556Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:36:24.556Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:36:24.556Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20562.220 ms) ======
[2024-11-17T22:36:24.556Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-17T22:36:24.556Z] GC before operation: completed in 93.082 ms, heap usage 128.403 MB -> 50.164 MB.
[2024-11-17T22:36:27.922Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:36:30.398Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:36:33.634Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:36:36.823Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:36:38.095Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:36:39.964Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:36:41.823Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:36:43.663Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:36:44.058Z] 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.
[2024-11-17T22:36:44.058Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:36:44.058Z] Movies recommended for you:
[2024-11-17T22:36:44.058Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:36:44.058Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:36:44.058Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19748.903 ms) ======
[2024-11-17T22:36:44.058Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-17T22:36:44.436Z] GC before operation: completed in 96.512 ms, heap usage 152.581 MB -> 49.962 MB.
[2024-11-17T22:36:47.734Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:36:50.175Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:36:53.434Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:36:55.910Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:36:57.756Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:36:59.588Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:37:01.423Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:37:03.273Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:37:03.273Z] 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.
[2024-11-17T22:37:03.273Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:37:03.637Z] Movies recommended for you:
[2024-11-17T22:37:03.637Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:37:03.637Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:37:03.637Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19255.941 ms) ======
[2024-11-17T22:37:03.637Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-17T22:37:03.637Z] GC before operation: completed in 92.138 ms, heap usage 202.372 MB -> 50.172 MB.
[2024-11-17T22:37:06.898Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:37:10.094Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:37:12.575Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:37:15.794Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:37:17.062Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:37:18.887Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:37:21.374Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:37:22.679Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:37:23.063Z] 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.
[2024-11-17T22:37:23.063Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:37:23.063Z] Movies recommended for you:
[2024-11-17T22:37:23.063Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:37:23.063Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:37:23.063Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19517.531 ms) ======
[2024-11-17T22:37:23.063Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-17T22:37:23.436Z] GC before operation: completed in 101.663 ms, heap usage 153.960 MB -> 50.203 MB.
[2024-11-17T22:37:26.645Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:37:29.863Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:37:32.338Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:37:35.576Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:37:36.857Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:37:38.676Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:37:40.554Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:37:42.385Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:37:42.756Z] 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.
[2024-11-17T22:37:42.756Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:37:42.756Z] Movies recommended for you:
[2024-11-17T22:37:42.756Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:37:42.756Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:37:42.756Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19563.108 ms) ======
[2024-11-17T22:37:42.756Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-17T22:37:43.122Z] GC before operation: completed in 96.136 ms, heap usage 123.962 MB -> 50.002 MB.
[2024-11-17T22:37:46.353Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:37:48.884Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:37:52.146Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:37:54.626Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:37:56.445Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:37:58.284Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:38:00.152Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:38:02.006Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:38:02.371Z] 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.
[2024-11-17T22:38:02.371Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:38:02.371Z] Movies recommended for you:
[2024-11-17T22:38:02.371Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:38:02.371Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:38:02.371Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19420.894 ms) ======
[2024-11-17T22:38:02.371Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-17T22:38:02.371Z] GC before operation: completed in 92.487 ms, heap usage 122.396 MB -> 50.061 MB.
[2024-11-17T22:38:05.577Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:38:08.784Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:38:11.252Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:38:14.555Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:38:15.903Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:38:17.874Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:38:19.716Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:38:21.545Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:38:21.545Z] 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.
[2024-11-17T22:38:21.545Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:38:21.922Z] Movies recommended for you:
[2024-11-17T22:38:21.922Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:38:21.922Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:38:21.922Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19352.933 ms) ======
[2024-11-17T22:38:21.922Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-17T22:38:21.922Z] GC before operation: completed in 92.602 ms, heap usage 267.658 MB -> 50.382 MB.
[2024-11-17T22:38:25.131Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:38:28.329Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:38:30.848Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:38:34.050Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:38:35.342Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:38:37.153Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:38:39.007Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:38:40.827Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:38:41.193Z] 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.
[2024-11-17T22:38:41.193Z] The best model improves the baseline by 14.52%.
[2024-11-17T22:38:41.193Z] Movies recommended for you:
[2024-11-17T22:38:41.193Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:38:41.193Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:38:41.193Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19440.874 ms) ======
[2024-11-17T22:38:41.967Z] -----------------------------------
[2024-11-17T22:38:41.967Z] renaissance-movie-lens_0_PASSED
[2024-11-17T22:38:41.967Z] -----------------------------------
[2024-11-17T22:38:41.967Z]
[2024-11-17T22:38:41.967Z] TEST TEARDOWN:
[2024-11-17T22:38:41.967Z] Nothing to be done for teardown.
[2024-11-17T22:38:41.967Z] renaissance-movie-lens_0 Finish Time: Sun Nov 17 22:38:41 2024 Epoch Time (ms): 1731883121790