renaissance-movie-lens_0
[2024-10-03T07:44:11.712Z] Running test renaissance-movie-lens_0 ...
[2024-10-03T07:44:11.712Z] ===============================================
[2024-10-03T07:44:11.712Z] renaissance-movie-lens_0 Start Time: Thu Oct 3 07:44:11 2024 Epoch Time (ms): 1727941451455
[2024-10-03T07:44:11.712Z] variation: NoOptions
[2024-10-03T07:44:11.712Z] JVM_OPTIONS:
[2024-10-03T07:44:11.712Z] { \
[2024-10-03T07:44:11.712Z] echo ""; echo "TEST SETUP:"; \
[2024-10-03T07:44:11.712Z] echo "Nothing to be done for setup."; \
[2024-10-03T07:44:11.712Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17279402866251/renaissance-movie-lens_0"; \
[2024-10-03T07:44:11.712Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17279402866251/renaissance-movie-lens_0"; \
[2024-10-03T07:44:11.712Z] echo ""; echo "TESTING:"; \
[2024-10-03T07:44:11.712Z] "/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_17279402866251/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-03T07:44:11.712Z] 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_17279402866251/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-03T07:44:11.712Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-03T07:44:11.712Z] echo "Nothing to be done for teardown."; \
[2024-10-03T07:44:11.712Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17279402866251/TestTargetResult";
[2024-10-03T07:44:11.712Z]
[2024-10-03T07:44:11.712Z] TEST SETUP:
[2024-10-03T07:44:11.712Z] Nothing to be done for setup.
[2024-10-03T07:44:11.712Z]
[2024-10-03T07:44:11.712Z] TESTING:
[2024-10-03T07:44:15.194Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-03T07:44:16.834Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-03T07:44:21.410Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-03T07:44:22.208Z] Training: 60056, validation: 20285, test: 19854
[2024-10-03T07:44:22.208Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-03T07:44:22.208Z] GC before operation: completed in 103.683 ms, heap usage 86.816 MB -> 36.440 MB.
[2024-10-03T07:44:32.326Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:44:39.390Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:44:42.910Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:44:47.478Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:44:50.136Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:44:52.846Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:44:55.372Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:44:57.922Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:44:57.923Z] 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.
[2024-10-03T07:44:57.923Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:44:58.712Z] Movies recommended for you:
[2024-10-03T07:44:58.712Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:44:58.712Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:44:58.712Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (36366.176 ms) ======
[2024-10-03T07:44:58.712Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-03T07:44:58.712Z] GC before operation: completed in 158.586 ms, heap usage 209.795 MB -> 49.063 MB.
[2024-10-03T07:45:03.274Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:45:06.770Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:45:11.363Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:45:14.872Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:45:16.502Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:45:19.034Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:45:21.555Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:45:23.192Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:45:23.982Z] 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.
[2024-10-03T07:45:23.982Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:45:23.982Z] Movies recommended for you:
[2024-10-03T07:45:23.982Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:45:23.982Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:45:23.982Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (25376.567 ms) ======
[2024-10-03T07:45:23.982Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-03T07:45:23.982Z] GC before operation: completed in 129.041 ms, heap usage 130.447 MB -> 48.972 MB.
[2024-10-03T07:45:27.504Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:45:31.045Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:45:35.598Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:45:39.143Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:45:40.768Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:45:43.302Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:45:45.823Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:45:47.454Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:45:48.246Z] 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.
[2024-10-03T07:45:48.246Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:45:48.246Z] Movies recommended for you:
[2024-10-03T07:45:48.246Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:45:48.246Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:45:48.246Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (24083.184 ms) ======
[2024-10-03T07:45:48.246Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-03T07:45:48.246Z] GC before operation: completed in 134.635 ms, heap usage 142.476 MB -> 49.278 MB.
[2024-10-03T07:45:52.350Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:45:54.880Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:45:58.382Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:46:01.898Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:46:03.531Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:46:06.062Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:46:08.596Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:46:10.226Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:46:10.226Z] 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.
[2024-10-03T07:46:10.226Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:46:10.226Z] Movies recommended for you:
[2024-10-03T07:46:10.226Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:46:10.226Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:46:10.226Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (22164.986 ms) ======
[2024-10-03T07:46:10.226Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-03T07:46:10.226Z] GC before operation: completed in 103.963 ms, heap usage 193.125 MB -> 49.648 MB.
[2024-10-03T07:46:13.728Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:46:17.225Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:46:20.750Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:46:24.268Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:46:25.916Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:46:28.433Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:46:30.192Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:46:32.717Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:46:32.717Z] 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.
[2024-10-03T07:46:32.717Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:46:32.717Z] Movies recommended for you:
[2024-10-03T07:46:32.717Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:46:32.717Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:46:32.717Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (22097.409 ms) ======
[2024-10-03T07:46:32.717Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-03T07:46:32.717Z] GC before operation: completed in 96.254 ms, heap usage 169.023 MB -> 49.843 MB.
[2024-10-03T07:46:36.201Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:46:38.721Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:46:42.210Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:46:44.743Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:46:46.367Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:46:48.896Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:46:50.522Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:46:53.056Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:46:53.056Z] 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.
[2024-10-03T07:46:53.056Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:46:53.056Z] Movies recommended for you:
[2024-10-03T07:46:53.056Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:46:53.056Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:46:53.056Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20372.483 ms) ======
[2024-10-03T07:46:53.056Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-03T07:46:53.056Z] GC before operation: completed in 127.800 ms, heap usage 213.694 MB -> 49.777 MB.
[2024-10-03T07:46:56.551Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:47:00.586Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:47:04.075Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:47:07.562Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:47:10.088Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:47:11.715Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:47:14.231Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:47:16.761Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:47:16.761Z] 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.
[2024-10-03T07:47:16.761Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:47:16.761Z] Movies recommended for you:
[2024-10-03T07:47:16.761Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:47:16.761Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:47:16.761Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (23520.939 ms) ======
[2024-10-03T07:47:16.761Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-03T07:47:16.761Z] GC before operation: completed in 133.255 ms, heap usage 227.260 MB -> 49.970 MB.
[2024-10-03T07:47:20.264Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:47:23.753Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:47:29.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:47:32.932Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:47:34.574Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:47:36.205Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:47:38.722Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:47:40.344Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:47:40.344Z] 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.
[2024-10-03T07:47:40.344Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:47:40.344Z] Movies recommended for you:
[2024-10-03T07:47:40.344Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:47:40.344Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:47:40.344Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (23763.503 ms) ======
[2024-10-03T07:47:40.344Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-03T07:47:40.344Z] GC before operation: completed in 104.003 ms, heap usage 131.513 MB -> 50.130 MB.
[2024-10-03T07:47:43.851Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:47:46.369Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:47:50.940Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:47:55.509Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:47:57.125Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:47:58.742Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:48:00.359Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:48:02.525Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:48:02.525Z] 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.
[2024-10-03T07:48:02.525Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:48:02.525Z] Movies recommended for you:
[2024-10-03T07:48:02.525Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:48:02.525Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:48:02.525Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (21675.958 ms) ======
[2024-10-03T07:48:02.525Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-03T07:48:02.525Z] GC before operation: completed in 102.026 ms, heap usage 208.328 MB -> 50.047 MB.
[2024-10-03T07:48:06.034Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:48:08.549Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:48:11.073Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:48:14.558Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:48:16.176Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:48:17.795Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:48:19.421Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:48:21.042Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:48:21.042Z] 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.
[2024-10-03T07:48:21.042Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:48:21.042Z] Movies recommended for you:
[2024-10-03T07:48:21.042Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:48:21.042Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:48:21.042Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (18767.432 ms) ======
[2024-10-03T07:48:21.042Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-03T07:48:21.042Z] GC before operation: completed in 105.420 ms, heap usage 179.612 MB -> 50.144 MB.
[2024-10-03T07:48:24.574Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:48:28.339Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:48:33.194Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:48:36.944Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:48:39.762Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:48:42.479Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:48:45.192Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:48:47.903Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:48:47.903Z] 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.
[2024-10-03T07:48:47.903Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:48:48.758Z] Movies recommended for you:
[2024-10-03T07:48:48.758Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:48:48.758Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:48:48.758Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (26974.093 ms) ======
[2024-10-03T07:48:48.758Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-03T07:48:48.758Z] GC before operation: completed in 160.796 ms, heap usage 142.745 MB -> 49.843 MB.
[2024-10-03T07:48:53.633Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:48:58.514Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:49:05.500Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:49:10.027Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:49:12.551Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:49:15.057Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:49:18.509Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:49:21.006Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:49:21.782Z] 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.
[2024-10-03T07:49:21.782Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:49:21.782Z] Movies recommended for you:
[2024-10-03T07:49:21.782Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:49:21.782Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:49:21.782Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (33354.119 ms) ======
[2024-10-03T07:49:21.782Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-03T07:49:21.782Z] GC before operation: completed in 127.391 ms, heap usage 181.507 MB -> 50.063 MB.
[2024-10-03T07:49:28.707Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:49:31.202Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:49:36.875Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:49:40.342Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:49:42.883Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:49:46.359Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:49:49.001Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:49:53.512Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:49:53.512Z] 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.
[2024-10-03T07:49:53.512Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:49:53.512Z] Movies recommended for you:
[2024-10-03T07:49:53.512Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:49:53.512Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:49:53.512Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (31446.163 ms) ======
[2024-10-03T07:49:53.512Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-03T07:49:53.512Z] GC before operation: completed in 128.779 ms, heap usage 251.015 MB -> 55.747 MB.
[2024-10-03T07:49:56.949Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:50:00.382Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:50:02.876Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:50:06.302Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:50:07.897Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:50:10.372Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:50:11.958Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:50:13.566Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:50:14.907Z] 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.
[2024-10-03T07:50:14.907Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:50:14.907Z] Movies recommended for you:
[2024-10-03T07:50:14.907Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:50:14.907Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:50:14.907Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (20540.306 ms) ======
[2024-10-03T07:50:14.907Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-03T07:50:14.907Z] GC before operation: completed in 113.156 ms, heap usage 182.642 MB -> 50.000 MB.
[2024-10-03T07:50:17.387Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:50:20.814Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:50:24.248Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:50:28.760Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:50:31.246Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:50:34.725Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:50:38.201Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:50:40.698Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:50:40.698Z] 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.
[2024-10-03T07:50:40.698Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:50:41.477Z] Movies recommended for you:
[2024-10-03T07:50:41.477Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:50:41.477Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:50:41.477Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (26930.755 ms) ======
[2024-10-03T07:50:41.477Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-03T07:50:41.477Z] GC before operation: completed in 229.211 ms, heap usage 79.963 MB -> 49.998 MB.
[2024-10-03T07:50:47.211Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:50:51.735Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:50:57.387Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:51:00.841Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:51:04.297Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:51:06.823Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:51:11.338Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:51:13.833Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:51:13.833Z] 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.
[2024-10-03T07:51:13.833Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:51:14.624Z] Movies recommended for you:
[2024-10-03T07:51:14.624Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:51:14.624Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:51:14.624Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (32974.483 ms) ======
[2024-10-03T07:51:14.624Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-03T07:51:14.624Z] GC before operation: completed in 199.505 ms, heap usage 125.042 MB -> 50.142 MB.
[2024-10-03T07:51:18.071Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:51:22.723Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:51:26.184Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:51:29.621Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:51:32.098Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:51:33.710Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:51:35.325Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:51:36.921Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:51:37.696Z] 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.
[2024-10-03T07:51:37.696Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:51:37.696Z] Movies recommended for you:
[2024-10-03T07:51:37.696Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:51:37.696Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:51:37.696Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (23145.662 ms) ======
[2024-10-03T07:51:37.696Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-03T07:51:37.696Z] GC before operation: completed in 201.516 ms, heap usage 225.958 MB -> 50.108 MB.
[2024-10-03T07:51:41.126Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:51:44.577Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:51:49.084Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:51:51.564Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:51:54.045Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:51:55.642Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:51:58.126Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:51:59.738Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:52:00.510Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-03T07:52:00.510Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:52:00.510Z] Movies recommended for you:
[2024-10-03T07:52:00.510Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:52:00.510Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:52:00.510Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (22560.010 ms) ======
[2024-10-03T07:52:00.510Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-03T07:52:00.510Z] GC before operation: completed in 137.572 ms, heap usage 224.225 MB -> 50.125 MB.
[2024-10-03T07:52:04.985Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:52:08.448Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:52:11.902Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:52:16.391Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:52:17.987Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:52:20.472Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:52:22.951Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:52:25.074Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:52:25.850Z] 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.
[2024-10-03T07:52:25.850Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:52:25.850Z] Movies recommended for you:
[2024-10-03T07:52:25.850Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:52:25.850Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:52:25.850Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (25250.415 ms) ======
[2024-10-03T07:52:25.850Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-03T07:52:25.850Z] GC before operation: completed in 151.802 ms, heap usage 174.226 MB -> 50.355 MB.
[2024-10-03T07:52:30.353Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T07:52:33.789Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T07:52:38.271Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T07:52:41.708Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T07:52:44.186Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T07:52:45.788Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T07:52:48.283Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T07:52:49.886Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T07:52:50.654Z] 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.
[2024-10-03T07:52:50.654Z] The best model improves the baseline by 14.52%.
[2024-10-03T07:52:50.654Z] Movies recommended for you:
[2024-10-03T07:52:50.654Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T07:52:50.654Z] There is no way to check that no silent failure occurred.
[2024-10-03T07:52:50.654Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (24626.208 ms) ======
[2024-10-03T07:52:51.424Z] -----------------------------------
[2024-10-03T07:52:51.424Z] renaissance-movie-lens_0_PASSED
[2024-10-03T07:52:51.424Z] -----------------------------------
[2024-10-03T07:52:51.424Z]
[2024-10-03T07:52:51.424Z] TEST TEARDOWN:
[2024-10-03T07:52:51.424Z] Nothing to be done for teardown.
[2024-10-03T07:52:51.424Z] renaissance-movie-lens_0 Finish Time: Thu Oct 3 07:52:50 2024 Epoch Time (ms): 1727941970665