renaissance-movie-lens_0
[2025-02-05T22:54:30.959Z] Running test renaissance-movie-lens_0 ...
[2025-02-05T22:54:30.959Z] ===============================================
[2025-02-05T22:54:30.959Z] renaissance-movie-lens_0 Start Time: Wed Feb 5 22:54:29 2025 Epoch Time (ms): 1738796069040
[2025-02-05T22:54:30.959Z] variation: NoOptions
[2025-02-05T22:54:30.959Z] JVM_OPTIONS:
[2025-02-05T22:54:30.959Z] { \
[2025-02-05T22:54:30.959Z] echo ""; echo "TEST SETUP:"; \
[2025-02-05T22:54:30.959Z] echo "Nothing to be done for setup."; \
[2025-02-05T22:54:30.959Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17387948919696/renaissance-movie-lens_0"; \
[2025-02-05T22:54:30.959Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17387948919696/renaissance-movie-lens_0"; \
[2025-02-05T22:54:30.959Z] echo ""; echo "TESTING:"; \
[2025-02-05T22:54:30.959Z] "/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_17387948919696/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-05T22:54:30.959Z] 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_17387948919696/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-05T22:54:30.959Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-05T22:54:30.959Z] echo "Nothing to be done for teardown."; \
[2025-02-05T22:54:30.959Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17387948919696/TestTargetResult";
[2025-02-05T22:54:30.959Z]
[2025-02-05T22:54:30.959Z] TEST SETUP:
[2025-02-05T22:54:30.959Z] Nothing to be done for setup.
[2025-02-05T22:54:30.959Z]
[2025-02-05T22:54:30.959Z] TESTING:
[2025-02-05T22:54:33.468Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-05T22:54:36.771Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-05T22:54:42.164Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-05T22:54:42.164Z] Training: 60056, validation: 20285, test: 19854
[2025-02-05T22:54:42.164Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-05T22:54:42.164Z] GC before operation: completed in 77.901 ms, heap usage 111.110 MB -> 36.435 MB.
[2025-02-05T22:54:51.849Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:54:57.347Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:55:01.610Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:55:06.854Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:55:08.763Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:55:10.712Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:55:13.267Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:55:15.835Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:55:15.835Z] 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.
[2025-02-05T22:55:16.215Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:55:16.215Z] Movies recommended for you:
[2025-02-05T22:55:16.215Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:55:16.215Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:55:16.215Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (34393.209 ms) ======
[2025-02-05T22:55:16.215Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-05T22:55:16.215Z] GC before operation: completed in 99.387 ms, heap usage 242.007 MB -> 49.064 MB.
[2025-02-05T22:55:20.403Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:55:23.744Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:55:27.982Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:55:30.660Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:55:33.260Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:55:35.190Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:55:37.119Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:55:39.016Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:55:39.409Z] 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.
[2025-02-05T22:55:39.409Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:55:39.797Z] Movies recommended for you:
[2025-02-05T22:55:39.797Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:55:39.797Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:55:39.797Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (23304.949 ms) ======
[2025-02-05T22:55:39.797Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-05T22:55:39.797Z] GC before operation: completed in 91.157 ms, heap usage 155.429 MB -> 49.025 MB.
[2025-02-05T22:55:43.120Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:55:47.345Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:55:50.727Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:55:53.295Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:55:55.889Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:55:57.212Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:55:59.947Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:56:01.881Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:56:01.881Z] 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.
[2025-02-05T22:56:01.881Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:56:01.881Z] Movies recommended for you:
[2025-02-05T22:56:01.881Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:56:01.881Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:56:01.881Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (22245.957 ms) ======
[2025-02-05T22:56:01.881Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-05T22:56:02.278Z] GC before operation: completed in 94.068 ms, heap usage 225.707 MB -> 49.367 MB.
[2025-02-05T22:56:05.598Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:56:08.963Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:56:12.257Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:56:15.588Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:56:18.160Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:56:20.125Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:56:22.090Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:56:23.982Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:56:23.982Z] 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.
[2025-02-05T22:56:23.982Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:56:24.380Z] Movies recommended for you:
[2025-02-05T22:56:24.380Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:56:24.380Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:56:24.380Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (22091.233 ms) ======
[2025-02-05T22:56:24.380Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-05T22:56:24.380Z] GC before operation: completed in 94.211 ms, heap usage 182.650 MB -> 49.654 MB.
[2025-02-05T22:56:27.761Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:56:31.100Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:56:33.966Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:56:37.269Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:56:39.172Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:56:41.082Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:56:43.681Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:56:45.034Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:56:45.449Z] 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.
[2025-02-05T22:56:45.449Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:56:45.449Z] Movies recommended for you:
[2025-02-05T22:56:45.449Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:56:45.449Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:56:45.449Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21250.630 ms) ======
[2025-02-05T22:56:45.449Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-05T22:56:45.836Z] GC before operation: completed in 92.989 ms, heap usage 64.599 MB -> 49.732 MB.
[2025-02-05T22:56:49.158Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:56:51.746Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:56:55.061Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:56:57.597Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:56:59.516Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:57:02.127Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:57:03.489Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:57:05.391Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:57:05.793Z] 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.
[2025-02-05T22:57:05.793Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:57:06.180Z] Movies recommended for you:
[2025-02-05T22:57:06.180Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:57:06.180Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:57:06.180Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20317.100 ms) ======
[2025-02-05T22:57:06.180Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-05T22:57:06.180Z] GC before operation: completed in 92.716 ms, heap usage 202.262 MB -> 49.788 MB.
[2025-02-05T22:57:09.478Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:57:12.023Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:57:15.369Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:57:18.041Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:57:19.938Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:57:21.879Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:57:23.791Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:57:25.126Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:57:25.509Z] 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.
[2025-02-05T22:57:25.509Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:57:25.897Z] Movies recommended for you:
[2025-02-05T22:57:25.897Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:57:25.897Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:57:25.897Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (19706.591 ms) ======
[2025-02-05T22:57:25.897Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-05T22:57:25.897Z] GC before operation: completed in 92.547 ms, heap usage 217.952 MB -> 49.960 MB.
[2025-02-05T22:57:29.239Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:57:31.806Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:57:35.242Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:57:37.827Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:57:39.165Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:57:41.082Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:57:42.994Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:57:44.886Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:57:44.886Z] 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.
[2025-02-05T22:57:44.886Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:57:45.286Z] Movies recommended for you:
[2025-02-05T22:57:45.286Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:57:45.286Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:57:45.286Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19328.199 ms) ======
[2025-02-05T22:57:45.286Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-05T22:57:45.286Z] GC before operation: completed in 92.022 ms, heap usage 177.961 MB -> 50.215 MB.
[2025-02-05T22:57:48.699Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:57:51.270Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:57:54.586Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:57:57.280Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:57:58.674Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:58:00.592Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:58:02.486Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:58:04.463Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:58:04.463Z] 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.
[2025-02-05T22:58:04.463Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:58:04.847Z] Movies recommended for you:
[2025-02-05T22:58:04.847Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:58:04.847Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:58:04.847Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19414.699 ms) ======
[2025-02-05T22:58:04.847Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-05T22:58:04.847Z] GC before operation: completed in 94.330 ms, heap usage 197.830 MB -> 50.047 MB.
[2025-02-05T22:58:08.157Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:58:10.697Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:58:14.058Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:58:16.612Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:58:18.509Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:58:20.431Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:58:22.343Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:58:23.674Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:58:24.062Z] 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.
[2025-02-05T22:58:24.062Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:58:24.062Z] Movies recommended for you:
[2025-02-05T22:58:24.062Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:58:24.062Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:58:24.062Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19415.635 ms) ======
[2025-02-05T22:58:24.062Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-05T22:58:24.447Z] GC before operation: completed in 94.854 ms, heap usage 285.061 MB -> 50.212 MB.
[2025-02-05T22:58:27.933Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:58:30.471Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:58:33.768Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:58:36.496Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:58:37.814Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:58:39.732Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:58:41.643Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:58:43.554Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:58:43.554Z] 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.
[2025-02-05T22:58:43.933Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:58:43.933Z] Movies recommended for you:
[2025-02-05T22:58:43.933Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:58:43.933Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:58:43.933Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19536.000 ms) ======
[2025-02-05T22:58:43.933Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-05T22:58:43.933Z] GC before operation: completed in 94.940 ms, heap usage 191.224 MB -> 49.953 MB.
[2025-02-05T22:58:47.295Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:58:49.810Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:58:53.199Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:58:56.033Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:58:57.393Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:58:59.298Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:59:01.194Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:59:03.131Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:59:03.131Z] 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.
[2025-02-05T22:59:03.131Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:59:03.131Z] Movies recommended for you:
[2025-02-05T22:59:03.131Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:59:03.131Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:59:03.131Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19343.141 ms) ======
[2025-02-05T22:59:03.131Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-05T22:59:03.518Z] GC before operation: completed in 95.300 ms, heap usage 198.996 MB -> 50.108 MB.
[2025-02-05T22:59:06.073Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:59:09.457Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:59:12.091Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:59:15.497Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:59:16.938Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:59:18.285Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:59:20.217Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:59:22.823Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:59:22.823Z] 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.
[2025-02-05T22:59:22.823Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:59:22.823Z] Movies recommended for you:
[2025-02-05T22:59:22.823Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:59:22.823Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:59:22.823Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (19474.222 ms) ======
[2025-02-05T22:59:22.823Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-05T22:59:22.823Z] GC before operation: completed in 94.873 ms, heap usage 217.968 MB -> 50.267 MB.
[2025-02-05T22:59:26.206Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:59:29.693Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:59:32.268Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:59:35.595Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:59:36.953Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:59:38.917Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T22:59:40.239Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T22:59:42.161Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T22:59:42.541Z] 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.
[2025-02-05T22:59:42.541Z] The best model improves the baseline by 14.52%.
[2025-02-05T22:59:42.541Z] Movies recommended for you:
[2025-02-05T22:59:42.541Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T22:59:42.541Z] There is no way to check that no silent failure occurred.
[2025-02-05T22:59:42.541Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19617.834 ms) ======
[2025-02-05T22:59:42.541Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-05T22:59:42.541Z] GC before operation: completed in 97.081 ms, heap usage 194.622 MB -> 50.014 MB.
[2025-02-05T22:59:45.850Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T22:59:48.387Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T22:59:51.717Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T22:59:54.405Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T22:59:56.331Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T22:59:58.292Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T23:00:00.219Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T23:00:01.713Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T23:00:02.151Z] 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.
[2025-02-05T23:00:02.151Z] The best model improves the baseline by 14.52%.
[2025-02-05T23:00:02.151Z] Movies recommended for you:
[2025-02-05T23:00:02.151Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T23:00:02.151Z] There is no way to check that no silent failure occurred.
[2025-02-05T23:00:02.151Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19421.114 ms) ======
[2025-02-05T23:00:02.151Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-05T23:00:02.151Z] GC before operation: completed in 94.279 ms, heap usage 182.257 MB -> 50.184 MB.
[2025-02-05T23:00:05.483Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T23:00:08.301Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T23:00:11.664Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T23:00:14.279Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T23:00:15.675Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T23:00:17.629Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T23:00:19.101Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T23:00:21.030Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T23:00:21.416Z] 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.
[2025-02-05T23:00:21.416Z] The best model improves the baseline by 14.52%.
[2025-02-05T23:00:21.416Z] Movies recommended for you:
[2025-02-05T23:00:21.416Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T23:00:21.417Z] There is no way to check that no silent failure occurred.
[2025-02-05T23:00:21.417Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19230.583 ms) ======
[2025-02-05T23:00:21.417Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-05T23:00:21.417Z] GC before operation: completed in 92.288 ms, heap usage 175.301 MB -> 50.259 MB.
[2025-02-05T23:00:24.779Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T23:00:27.333Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T23:00:30.668Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T23:00:33.353Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T23:00:35.280Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T23:00:36.639Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T23:00:38.572Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T23:00:40.498Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T23:00:40.498Z] 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.
[2025-02-05T23:00:40.885Z] The best model improves the baseline by 14.52%.
[2025-02-05T23:00:40.885Z] Movies recommended for you:
[2025-02-05T23:00:40.885Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T23:00:40.885Z] There is no way to check that no silent failure occurred.
[2025-02-05T23:00:40.885Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19312.230 ms) ======
[2025-02-05T23:00:40.885Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-05T23:00:40.885Z] GC before operation: completed in 108.151 ms, heap usage 208.448 MB -> 50.111 MB.
[2025-02-05T23:00:44.250Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T23:00:46.833Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T23:00:50.177Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T23:00:52.732Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T23:00:54.624Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T23:00:55.947Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T23:00:57.853Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T23:00:59.958Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T23:00:59.958Z] 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.
[2025-02-05T23:00:59.958Z] The best model improves the baseline by 14.52%.
[2025-02-05T23:01:00.340Z] Movies recommended for you:
[2025-02-05T23:01:00.340Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T23:01:00.340Z] There is no way to check that no silent failure occurred.
[2025-02-05T23:01:00.340Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19211.302 ms) ======
[2025-02-05T23:01:00.340Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-05T23:01:00.340Z] GC before operation: completed in 102.110 ms, heap usage 182.744 MB -> 50.168 MB.
[2025-02-05T23:01:03.661Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T23:01:06.287Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T23:01:09.594Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T23:01:12.188Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T23:01:14.089Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T23:01:15.420Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T23:01:17.323Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T23:01:19.250Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T23:01:19.643Z] 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.
[2025-02-05T23:01:19.643Z] The best model improves the baseline by 14.52%.
[2025-02-05T23:01:19.643Z] Movies recommended for you:
[2025-02-05T23:01:19.643Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T23:01:19.643Z] There is no way to check that no silent failure occurred.
[2025-02-05T23:01:19.643Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19482.518 ms) ======
[2025-02-05T23:01:19.643Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-05T23:01:19.643Z] GC before operation: completed in 94.983 ms, heap usage 222.980 MB -> 50.341 MB.
[2025-02-05T23:01:22.955Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T23:01:25.624Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T23:01:28.953Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T23:01:31.487Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T23:01:33.551Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T23:01:34.902Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T23:01:36.822Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T23:01:38.734Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T23:01:39.114Z] 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.
[2025-02-05T23:01:39.114Z] The best model improves the baseline by 14.52%.
[2025-02-05T23:01:39.114Z] Movies recommended for you:
[2025-02-05T23:01:39.114Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T23:01:39.114Z] There is no way to check that no silent failure occurred.
[2025-02-05T23:01:39.114Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19281.932 ms) ======
[2025-02-05T23:01:39.923Z] -----------------------------------
[2025-02-05T23:01:39.923Z] renaissance-movie-lens_0_PASSED
[2025-02-05T23:01:39.923Z] -----------------------------------
[2025-02-05T23:01:39.923Z]
[2025-02-05T23:01:39.923Z] TEST TEARDOWN:
[2025-02-05T23:01:39.923Z] Nothing to be done for teardown.
[2025-02-05T23:01:39.923Z] renaissance-movie-lens_0 Finish Time: Wed Feb 5 23:01:39 2025 Epoch Time (ms): 1738796499550