renaissance-movie-lens_0
[2025-02-05T21:33:51.161Z] Running test renaissance-movie-lens_0 ...
[2025-02-05T21:33:51.161Z] ===============================================
[2025-02-05T21:33:51.161Z] renaissance-movie-lens_0 Start Time: Wed Feb 5 21:33:50 2025 Epoch Time (ms): 1738791230799
[2025-02-05T21:33:51.161Z] variation: NoOptions
[2025-02-05T21:33:51.161Z] JVM_OPTIONS:
[2025-02-05T21:33:51.161Z] { \
[2025-02-05T21:33:51.161Z] echo ""; echo "TEST SETUP:"; \
[2025-02-05T21:33:51.161Z] echo "Nothing to be done for setup."; \
[2025-02-05T21:33:51.161Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387903923399/renaissance-movie-lens_0"; \
[2025-02-05T21:33:51.161Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387903923399/renaissance-movie-lens_0"; \
[2025-02-05T21:33:51.161Z] echo ""; echo "TESTING:"; \
[2025-02-05T21:33:51.161Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387903923399/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-05T21:33:51.161Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387903923399/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-05T21:33:51.161Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-05T21:33:51.161Z] echo "Nothing to be done for teardown."; \
[2025-02-05T21:33:51.161Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387903923399/TestTargetResult";
[2025-02-05T21:33:51.161Z]
[2025-02-05T21:33:51.161Z] TEST SETUP:
[2025-02-05T21:33:51.161Z] Nothing to be done for setup.
[2025-02-05T21:33:51.161Z]
[2025-02-05T21:33:51.161Z] TESTING:
[2025-02-05T21:33:54.203Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-05T21:33:56.154Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-05T21:33:59.163Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-05T21:33:59.163Z] Training: 60056, validation: 20285, test: 19854
[2025-02-05T21:33:59.163Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-05T21:33:59.163Z] GC before operation: completed in 55.279 ms, heap usage 116.217 MB -> 37.112 MB.
[2025-02-05T21:34:03.330Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:34:06.346Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:34:10.060Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:34:12.016Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:34:13.967Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:34:14.918Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:34:16.870Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:34:17.819Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:34:18.771Z] 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.
[2025-02-05T21:34:18.771Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:34:18.771Z] Movies recommended for you:
[2025-02-05T21:34:18.771Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:34:18.771Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:34:18.771Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (19593.009 ms) ======
[2025-02-05T21:34:18.771Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-05T21:34:18.771Z] GC before operation: completed in 65.451 ms, heap usage 69.820 MB -> 54.574 MB.
[2025-02-05T21:34:20.730Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:34:23.740Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:34:25.728Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:34:27.688Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:34:29.645Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:34:30.606Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:34:32.605Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:34:33.554Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:34:33.554Z] 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.
[2025-02-05T21:34:33.554Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:34:34.503Z] Movies recommended for you:
[2025-02-05T21:34:34.503Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:34:34.503Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:34:34.503Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15523.914 ms) ======
[2025-02-05T21:34:34.503Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-05T21:34:34.503Z] GC before operation: completed in 76.131 ms, heap usage 356.471 MB -> 49.804 MB.
[2025-02-05T21:34:36.454Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:34:38.444Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:34:40.396Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:34:42.348Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:34:44.407Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:34:45.360Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:34:47.313Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:34:48.262Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:34:48.262Z] 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.
[2025-02-05T21:34:48.262Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:34:48.262Z] Movies recommended for you:
[2025-02-05T21:34:48.262Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:34:48.262Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:34:48.262Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14315.385 ms) ======
[2025-02-05T21:34:48.262Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-05T21:34:48.262Z] GC before operation: completed in 70.414 ms, heap usage 143.925 MB -> 49.965 MB.
[2025-02-05T21:34:50.219Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:34:52.170Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:34:55.354Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:34:57.325Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:34:58.281Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:34:59.305Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:35:01.277Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:35:02.230Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:35:02.230Z] 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.
[2025-02-05T21:35:02.230Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:35:02.230Z] Movies recommended for you:
[2025-02-05T21:35:02.230Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:35:02.230Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:35:02.230Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13856.889 ms) ======
[2025-02-05T21:35:02.230Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-05T21:35:02.230Z] GC before operation: completed in 68.272 ms, heap usage 142.362 MB -> 50.294 MB.
[2025-02-05T21:35:04.184Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:35:06.983Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:35:08.935Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:35:10.895Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:35:12.844Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:35:13.798Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:35:14.749Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:35:16.701Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:35:16.701Z] 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.
[2025-02-05T21:35:16.701Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:35:16.701Z] Movies recommended for you:
[2025-02-05T21:35:16.701Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:35:16.701Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:35:16.701Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14098.997 ms) ======
[2025-02-05T21:35:16.701Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-05T21:35:16.701Z] GC before operation: completed in 69.791 ms, heap usage 122.955 MB -> 50.453 MB.
[2025-02-05T21:35:18.648Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:35:20.620Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:35:22.576Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:35:24.526Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:35:26.475Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:35:27.425Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:35:28.375Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:35:29.325Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:35:30.279Z] 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.
[2025-02-05T21:35:30.279Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:35:30.279Z] Movies recommended for you:
[2025-02-05T21:35:30.279Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:35:30.279Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:35:30.279Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13469.649 ms) ======
[2025-02-05T21:35:30.279Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-05T21:35:30.279Z] GC before operation: completed in 65.486 ms, heap usage 317.051 MB -> 50.680 MB.
[2025-02-05T21:35:32.229Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:35:34.180Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:35:36.130Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:35:38.081Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:35:39.030Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:35:40.983Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:35:41.935Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:35:42.995Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:35:42.995Z] 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.
[2025-02-05T21:35:42.995Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:35:42.995Z] Movies recommended for you:
[2025-02-05T21:35:42.995Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:35:42.995Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:35:42.995Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (13171.604 ms) ======
[2025-02-05T21:35:42.995Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-05T21:35:42.995Z] GC before operation: completed in 73.518 ms, heap usage 319.273 MB -> 50.773 MB.
[2025-02-05T21:35:44.952Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:35:46.911Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:35:48.862Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:35:50.859Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:35:52.811Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:35:53.766Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:35:54.716Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:35:55.667Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:35:57.077Z] 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.
[2025-02-05T21:35:57.077Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:35:57.077Z] Movies recommended for you:
[2025-02-05T21:35:57.077Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:35:57.077Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:35:57.077Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12887.111 ms) ======
[2025-02-05T21:35:57.077Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-05T21:35:57.077Z] GC before operation: completed in 73.358 ms, heap usage 101.036 MB -> 50.900 MB.
[2025-02-05T21:35:58.030Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:35:59.987Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:36:01.975Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:36:03.938Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:36:04.889Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:36:06.851Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:36:07.802Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:36:08.753Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:36:08.753Z] 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.
[2025-02-05T21:36:09.736Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:36:09.736Z] Movies recommended for you:
[2025-02-05T21:36:09.736Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:36:09.736Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:36:09.736Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12924.731 ms) ======
[2025-02-05T21:36:09.736Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-05T21:36:09.736Z] GC before operation: completed in 77.437 ms, heap usage 95.686 MB -> 50.768 MB.
[2025-02-05T21:36:11.716Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:36:13.713Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:36:15.760Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:36:16.735Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:36:18.685Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:36:19.635Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:36:20.585Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:36:21.711Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:36:22.671Z] 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.
[2025-02-05T21:36:22.671Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:36:22.671Z] Movies recommended for you:
[2025-02-05T21:36:22.671Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:36:22.671Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:36:22.671Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12927.053 ms) ======
[2025-02-05T21:36:22.671Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-05T21:36:22.671Z] GC before operation: completed in 67.193 ms, heap usage 123.430 MB -> 50.889 MB.
[2025-02-05T21:36:24.622Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:36:26.608Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:36:28.558Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:36:30.525Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:36:31.484Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:36:32.436Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:36:33.392Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:36:35.343Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:36:35.343Z] 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.
[2025-02-05T21:36:35.343Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:36:35.343Z] Movies recommended for you:
[2025-02-05T21:36:35.343Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:36:35.343Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:36:35.343Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12979.413 ms) ======
[2025-02-05T21:36:35.343Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-05T21:36:35.343Z] GC before operation: completed in 67.381 ms, heap usage 86.107 MB -> 50.500 MB.
[2025-02-05T21:36:37.308Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:36:39.264Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:36:41.224Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:36:43.215Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:36:44.169Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:36:46.125Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:36:47.535Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:36:48.487Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:36:48.487Z] 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.
[2025-02-05T21:36:48.487Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:36:48.487Z] Movies recommended for you:
[2025-02-05T21:36:48.487Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:36:48.487Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:36:48.487Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12966.162 ms) ======
[2025-02-05T21:36:48.487Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-05T21:36:48.487Z] GC before operation: completed in 68.557 ms, heap usage 85.338 MB -> 50.794 MB.
[2025-02-05T21:36:50.538Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:36:52.493Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:36:54.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:36:56.403Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:36:57.361Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:36:59.321Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:37:00.271Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:37:01.237Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:37:02.187Z] 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.
[2025-02-05T21:37:02.187Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:37:02.187Z] Movies recommended for you:
[2025-02-05T21:37:02.187Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:37:02.187Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:37:02.187Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13507.146 ms) ======
[2025-02-05T21:37:02.187Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-05T21:37:02.187Z] GC before operation: completed in 90.645 ms, heap usage 253.648 MB -> 51.110 MB.
[2025-02-05T21:37:04.138Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:37:06.098Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:37:08.052Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:37:10.003Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:37:11.016Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:37:11.977Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:37:12.929Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:37:14.892Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:37:14.892Z] 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.
[2025-02-05T21:37:14.892Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:37:14.892Z] Movies recommended for you:
[2025-02-05T21:37:14.892Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:37:14.892Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:37:14.892Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12818.018 ms) ======
[2025-02-05T21:37:14.892Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-05T21:37:14.892Z] GC before operation: completed in 100.630 ms, heap usage 182.943 MB -> 50.801 MB.
[2025-02-05T21:37:16.869Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:37:18.826Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:37:20.775Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:37:22.725Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:37:23.684Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:37:25.636Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:37:26.585Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:37:27.545Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:37:27.545Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-05T21:37:27.545Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:37:27.545Z] Movies recommended for you:
[2025-02-05T21:37:27.545Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:37:27.545Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:37:27.545Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12980.859 ms) ======
[2025-02-05T21:37:27.545Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-05T21:37:28.497Z] GC before operation: completed in 83.740 ms, heap usage 122.454 MB -> 50.984 MB.
[2025-02-05T21:37:29.464Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:37:32.493Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:37:34.445Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:37:36.400Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:37:37.353Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:37:38.305Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:37:39.262Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:37:41.219Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:37:41.219Z] 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.
[2025-02-05T21:37:41.219Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:37:41.219Z] Movies recommended for you:
[2025-02-05T21:37:41.219Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:37:41.219Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:37:41.219Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13129.694 ms) ======
[2025-02-05T21:37:41.219Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-05T21:37:41.219Z] GC before operation: completed in 86.657 ms, heap usage 443.026 MB -> 54.450 MB.
[2025-02-05T21:37:43.170Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:37:46.009Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:37:46.959Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:37:48.910Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:37:49.867Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:37:51.819Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:37:52.770Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:37:53.722Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:37:53.722Z] 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.
[2025-02-05T21:37:53.722Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:37:54.672Z] Movies recommended for you:
[2025-02-05T21:37:54.672Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:37:54.672Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:37:54.672Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12914.310 ms) ======
[2025-02-05T21:37:54.672Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-05T21:37:54.672Z] GC before operation: completed in 80.247 ms, heap usage 346.118 MB -> 50.940 MB.
[2025-02-05T21:37:56.626Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:37:58.578Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:38:00.534Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:38:01.483Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:38:03.437Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:38:04.388Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:38:05.343Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:38:06.294Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:38:07.244Z] 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.
[2025-02-05T21:38:07.244Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:38:07.244Z] Movies recommended for you:
[2025-02-05T21:38:07.244Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:38:07.244Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:38:07.244Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12643.690 ms) ======
[2025-02-05T21:38:07.244Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-05T21:38:07.244Z] GC before operation: completed in 76.265 ms, heap usage 141.450 MB -> 50.804 MB.
[2025-02-05T21:38:09.197Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:38:11.148Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:38:13.106Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:38:15.083Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:38:16.040Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:38:16.992Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:38:17.944Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:38:18.909Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:38:19.917Z] 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.
[2025-02-05T21:38:19.917Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:38:19.917Z] Movies recommended for you:
[2025-02-05T21:38:19.917Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:38:19.917Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:38:19.917Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12529.748 ms) ======
[2025-02-05T21:38:19.917Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-05T21:38:19.917Z] GC before operation: completed in 70.116 ms, heap usage 91.472 MB -> 54.399 MB.
[2025-02-05T21:38:21.867Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:38:22.817Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:38:24.770Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:38:26.860Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:38:27.810Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:38:28.761Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:38:29.712Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:38:31.692Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:38:31.692Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-05T21:38:31.692Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:38:31.692Z] Movies recommended for you:
[2025-02-05T21:38:31.692Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:38:31.692Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:38:31.692Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (11920.775 ms) ======
[2025-02-05T21:38:31.692Z] -----------------------------------
[2025-02-05T21:38:31.692Z] renaissance-movie-lens_0_PASSED
[2025-02-05T21:38:31.692Z] -----------------------------------
[2025-02-05T21:38:31.692Z]
[2025-02-05T21:38:31.692Z] TEST TEARDOWN:
[2025-02-05T21:38:31.692Z] Nothing to be done for teardown.
[2025-02-05T21:38:31.692Z] renaissance-movie-lens_0 Finish Time: Wed Feb 5 21:38:31 2025 Epoch Time (ms): 1738791511514