renaissance-movie-lens_0
[2024-11-22T07:36:29.573Z] Running test renaissance-movie-lens_0 ...
[2024-11-22T07:36:29.573Z] ===============================================
[2024-11-22T07:36:29.573Z] renaissance-movie-lens_0 Start Time: Fri Nov 22 07:36:28 2024 Epoch Time (ms): 1732260988921
[2024-11-22T07:36:29.573Z] variation: NoOptions
[2024-11-22T07:36:29.573Z] JVM_OPTIONS:
[2024-11-22T07:36:29.573Z] { \
[2024-11-22T07:36:29.573Z] echo ""; echo "TEST SETUP:"; \
[2024-11-22T07:36:29.573Z] echo "Nothing to be done for setup."; \
[2024-11-22T07:36:29.574Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17322585752769/renaissance-movie-lens_0"; \
[2024-11-22T07:36:29.574Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17322585752769/renaissance-movie-lens_0"; \
[2024-11-22T07:36:29.574Z] echo ""; echo "TESTING:"; \
[2024-11-22T07:36:29.574Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-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_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17322585752769/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-22T07:36:29.574Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17322585752769/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-22T07:36:29.574Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-22T07:36:29.574Z] echo "Nothing to be done for teardown."; \
[2024-11-22T07:36:29.574Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17322585752769/TestTargetResult";
[2024-11-22T07:36:29.574Z]
[2024-11-22T07:36:29.574Z] TEST SETUP:
[2024-11-22T07:36:29.574Z] Nothing to be done for setup.
[2024-11-22T07:36:29.574Z]
[2024-11-22T07:36:29.574Z] TESTING:
[2024-11-22T07:36:37.854Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-22T07:36:46.059Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-22T07:37:02.631Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-22T07:37:03.383Z] Training: 60056, validation: 20285, test: 19854
[2024-11-22T07:37:03.383Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-22T07:37:03.383Z] GC before operation: completed in 376.861 ms, heap usage 71.589 MB -> 36.454 MB.
[2024-11-22T07:37:38.662Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:37:52.485Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:38:08.647Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:38:22.908Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:38:32.804Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:38:39.667Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:38:47.848Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:38:56.023Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:38:56.779Z] 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.
[2024-11-22T07:38:57.533Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:38:58.290Z] Movies recommended for you:
[2024-11-22T07:38:58.290Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:38:58.290Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:38:58.290Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (114653.456 ms) ======
[2024-11-22T07:38:58.290Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-22T07:38:59.039Z] GC before operation: completed in 587.863 ms, heap usage 201.670 MB -> 49.904 MB.
[2024-11-22T07:39:08.871Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:39:22.534Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:39:34.437Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:39:44.261Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:39:51.089Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:39:56.663Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:40:03.638Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:40:09.328Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:40:10.117Z] 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.
[2024-11-22T07:40:10.117Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:40:10.117Z] Movies recommended for you:
[2024-11-22T07:40:10.117Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:40:10.117Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:40:10.117Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (71630.439 ms) ======
[2024-11-22T07:40:10.117Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-22T07:40:10.900Z] GC before operation: completed in 181.041 ms, heap usage 142.559 MB -> 48.945 MB.
[2024-11-22T07:40:19.276Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:40:29.952Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:40:41.909Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:40:50.367Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:40:56.118Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:41:03.224Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:41:07.827Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:41:13.614Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:41:14.404Z] 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.
[2024-11-22T07:41:14.404Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:41:14.404Z] Movies recommended for you:
[2024-11-22T07:41:14.404Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:41:14.404Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:41:14.404Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (64178.653 ms) ======
[2024-11-22T07:41:14.404Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-22T07:41:15.204Z] GC before operation: completed in 442.018 ms, heap usage 171.856 MB -> 49.302 MB.
[2024-11-22T07:41:25.513Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:41:34.337Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:41:44.436Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:41:52.964Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:41:58.699Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:42:04.502Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:42:09.071Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:42:14.819Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:42:14.819Z] 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.
[2024-11-22T07:42:15.619Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:42:15.619Z] Movies recommended for you:
[2024-11-22T07:42:15.619Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:42:15.619Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:42:15.619Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (60360.231 ms) ======
[2024-11-22T07:42:15.619Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-22T07:42:15.619Z] GC before operation: completed in 284.578 ms, heap usage 144.098 MB -> 49.631 MB.
[2024-11-22T07:42:24.097Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:42:34.541Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:42:42.997Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:42:53.083Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:42:58.887Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:43:04.687Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:43:10.472Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:43:17.512Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:43:17.512Z] 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.
[2024-11-22T07:43:17.512Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:43:18.303Z] Movies recommended for you:
[2024-11-22T07:43:18.303Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:43:18.303Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:43:18.303Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (62265.745 ms) ======
[2024-11-22T07:43:18.303Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-22T07:43:18.303Z] GC before operation: completed in 384.288 ms, heap usage 157.192 MB -> 49.812 MB.
[2024-11-22T07:43:30.899Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:43:39.380Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:43:49.493Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:43:57.961Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:44:05.031Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:44:10.800Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:44:18.345Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:44:24.540Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:44:25.400Z] 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.
[2024-11-22T07:44:25.400Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:44:26.256Z] Movies recommended for you:
[2024-11-22T07:44:26.256Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:44:26.256Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:44:26.256Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (67508.982 ms) ======
[2024-11-22T07:44:26.256Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-22T07:44:26.256Z] GC before operation: completed in 421.739 ms, heap usage 200.967 MB -> 49.757 MB.
[2024-11-22T07:44:37.635Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:44:46.776Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:44:57.601Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:45:08.385Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:45:15.979Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:45:23.576Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:45:29.791Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:45:36.582Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:45:36.582Z] 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.
[2024-11-22T07:45:36.582Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:45:37.448Z] Movies recommended for you:
[2024-11-22T07:45:37.449Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:45:37.449Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:45:37.449Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (70815.836 ms) ======
[2024-11-22T07:45:37.449Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-22T07:45:37.449Z] GC before operation: completed in 360.919 ms, heap usage 107.539 MB -> 49.833 MB.
[2024-11-22T07:45:48.226Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:45:58.913Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:46:09.587Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:46:18.647Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:46:25.005Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:46:31.185Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:46:37.314Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:46:42.663Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:46:43.531Z] 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.
[2024-11-22T07:46:43.531Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:46:43.531Z] Movies recommended for you:
[2024-11-22T07:46:43.531Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:46:43.531Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:46:43.531Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (65949.285 ms) ======
[2024-11-22T07:46:43.531Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-22T07:46:43.531Z] GC before operation: completed in 343.575 ms, heap usage 211.594 MB -> 50.294 MB.
[2024-11-22T07:46:52.503Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:47:01.486Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:47:07.653Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:47:15.144Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:47:18.936Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:47:20.715Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:47:24.518Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:47:26.292Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:47:26.292Z] 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.
[2024-11-22T07:47:26.292Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:47:27.141Z] Movies recommended for you:
[2024-11-22T07:47:27.141Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:47:27.141Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:47:27.141Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (42901.457 ms) ======
[2024-11-22T07:47:27.141Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-22T07:47:27.141Z] GC before operation: completed in 222.274 ms, heap usage 141.365 MB -> 50.099 MB.
[2024-11-22T07:47:30.900Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:47:35.780Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:47:40.669Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:47:43.017Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:47:46.808Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:47:50.620Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:47:56.714Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:48:01.721Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:48:03.493Z] 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.
[2024-11-22T07:48:03.493Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:48:03.493Z] Movies recommended for you:
[2024-11-22T07:48:03.493Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:48:03.493Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:48:03.493Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (36274.813 ms) ======
[2024-11-22T07:48:03.493Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-22T07:48:03.493Z] GC before operation: completed in 392.608 ms, heap usage 201.004 MB -> 50.135 MB.
[2024-11-22T07:48:14.247Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:48:25.038Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:48:37.693Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:48:45.208Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:48:51.629Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:48:57.814Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:49:04.045Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:49:08.969Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:49:10.769Z] 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.
[2024-11-22T07:49:10.769Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:49:10.769Z] Movies recommended for you:
[2024-11-22T07:49:10.769Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:49:10.769Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:49:10.769Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (67160.732 ms) ======
[2024-11-22T07:49:10.769Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-22T07:49:11.641Z] GC before operation: completed in 469.097 ms, heap usage 101.844 MB -> 49.761 MB.
[2024-11-22T07:49:22.452Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:49:33.085Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:49:40.481Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:49:47.783Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:49:53.147Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:49:57.969Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:50:05.255Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:50:11.271Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:50:11.271Z] 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.
[2024-11-22T07:50:11.271Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:50:12.106Z] Movies recommended for you:
[2024-11-22T07:50:12.106Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:50:12.106Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:50:12.106Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (60647.523 ms) ======
[2024-11-22T07:50:12.106Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-22T07:50:12.106Z] GC before operation: completed in 384.948 ms, heap usage 205.945 MB -> 50.034 MB.
[2024-11-22T07:50:20.963Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:50:28.331Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:50:37.273Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:50:46.132Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:50:50.950Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:50:55.241Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:51:01.235Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:51:08.513Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:51:08.513Z] 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.
[2024-11-22T07:51:08.513Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:51:09.350Z] Movies recommended for you:
[2024-11-22T07:51:09.350Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:51:09.350Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:51:09.350Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (56929.794 ms) ======
[2024-11-22T07:51:09.350Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-22T07:51:10.203Z] GC before operation: completed in 474.733 ms, heap usage 126.587 MB -> 50.109 MB.
[2024-11-22T07:51:18.971Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:51:26.423Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:51:37.019Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:51:44.360Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:51:49.202Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:51:55.815Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:52:01.816Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:52:07.837Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:52:07.837Z] 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.
[2024-11-22T07:52:07.837Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:52:08.671Z] Movies recommended for you:
[2024-11-22T07:52:08.671Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:52:08.671Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:52:08.671Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (58752.870 ms) ======
[2024-11-22T07:52:08.671Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-22T07:52:08.671Z] GC before operation: completed in 352.117 ms, heap usage 105.806 MB -> 49.849 MB.
[2024-11-22T07:52:19.187Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:52:28.045Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:52:38.565Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:52:49.065Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:52:52.776Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:52:57.522Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:53:03.909Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:53:11.286Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:53:13.028Z] 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.
[2024-11-22T07:53:13.028Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:53:13.028Z] Movies recommended for you:
[2024-11-22T07:53:13.028Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:53:13.028Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:53:13.028Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (64330.899 ms) ======
[2024-11-22T07:53:13.028Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-22T07:53:13.887Z] GC before operation: completed in 373.724 ms, heap usage 149.893 MB -> 50.081 MB.
[2024-11-22T07:53:26.375Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:53:35.175Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:53:49.649Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:54:00.525Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:54:05.952Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:54:14.734Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:54:20.673Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:54:29.494Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:54:30.472Z] 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.
[2024-11-22T07:54:30.472Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:54:30.472Z] Movies recommended for you:
[2024-11-22T07:54:30.472Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:54:30.472Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:54:30.472Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (77132.732 ms) ======
[2024-11-22T07:54:30.472Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-22T07:54:32.682Z] GC before operation: completed in 440.940 ms, heap usage 227.749 MB -> 50.351 MB.
[2024-11-22T07:54:47.196Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:54:56.009Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:55:06.553Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:55:16.045Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:55:22.061Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:55:26.886Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:55:32.961Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:55:40.320Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:55:40.320Z] 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.
[2024-11-22T07:55:40.320Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:55:41.150Z] Movies recommended for you:
[2024-11-22T07:55:41.150Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:55:41.150Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:55:41.150Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (69826.958 ms) ======
[2024-11-22T07:55:41.150Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-22T07:55:41.150Z] GC before operation: completed in 435.361 ms, heap usage 107.637 MB -> 50.009 MB.
[2024-11-22T07:55:51.694Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:56:02.313Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:56:12.841Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:56:22.225Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:56:27.154Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:56:34.559Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:56:40.592Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:56:45.408Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:56:47.171Z] 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.
[2024-11-22T07:56:47.171Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:56:47.171Z] Movies recommended for you:
[2024-11-22T07:56:47.171Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:56:47.171Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:56:47.171Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (65779.244 ms) ======
[2024-11-22T07:56:47.171Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-22T07:56:48.008Z] GC before operation: completed in 483.560 ms, heap usage 165.893 MB -> 50.157 MB.
[2024-11-22T07:56:56.890Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:57:07.394Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:57:17.892Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:57:27.289Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:57:33.322Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:57:39.326Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:57:46.802Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:57:52.940Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:57:53.798Z] 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.
[2024-11-22T07:57:53.798Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:57:53.798Z] Movies recommended for you:
[2024-11-22T07:57:53.798Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:57:53.798Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:57:53.798Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (66259.133 ms) ======
[2024-11-22T07:57:53.798Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-22T07:57:54.653Z] GC before operation: completed in 473.388 ms, heap usage 195.502 MB -> 50.769 MB.
[2024-11-22T07:58:03.772Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T07:58:12.845Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T07:58:21.977Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T07:58:31.687Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T07:58:35.497Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T07:58:41.730Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T07:58:46.657Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T07:58:51.615Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T07:58:52.485Z] 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.
[2024-11-22T07:58:53.345Z] The best model improves the baseline by 14.52%.
[2024-11-22T07:58:53.345Z] Movies recommended for you:
[2024-11-22T07:58:53.345Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T07:58:53.345Z] There is no way to check that no silent failure occurred.
[2024-11-22T07:58:53.345Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (58838.347 ms) ======
[2024-11-22T07:58:55.114Z] -----------------------------------
[2024-11-22T07:58:55.114Z] renaissance-movie-lens_0_PASSED
[2024-11-22T07:58:55.114Z] -----------------------------------
[2024-11-22T07:58:55.114Z]
[2024-11-22T07:58:55.114Z] TEST TEARDOWN:
[2024-11-22T07:58:55.114Z] Nothing to be done for teardown.
[2024-11-22T07:58:55.114Z] renaissance-movie-lens_0 Finish Time: Fri Nov 22 07:58:54 2024 Epoch Time (ms): 1732262334302