renaissance-movie-lens_0
[2024-05-23T03:16:33.437Z] Running test renaissance-movie-lens_0 ...
[2024-05-23T03:16:33.437Z] ===============================================
[2024-05-23T03:16:33.437Z] renaissance-movie-lens_0 Start Time: Thu May 23 03:16:32 2024 Epoch Time (ms): 1716434192643
[2024-05-23T03:16:33.437Z] variation: NoOptions
[2024-05-23T03:16:33.437Z] JVM_OPTIONS:
[2024-05-23T03:16:33.437Z] { \
[2024-05-23T03:16:33.437Z] echo ""; echo "TEST SETUP:"; \
[2024-05-23T03:16:33.437Z] echo "Nothing to be done for setup."; \
[2024-05-23T03:16:33.437Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_alpine-linux/aqa-tests/TKG/../TKG/output_17164301966600/renaissance-movie-lens_0"; \
[2024-05-23T03:16:33.437Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_alpine-linux/aqa-tests/TKG/../TKG/output_17164301966600/renaissance-movie-lens_0"; \
[2024-05-23T03:16:33.438Z] echo ""; echo "TESTING:"; \
[2024-05-23T03:16:33.438Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_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_openjdk17_hs_extended.perf_aarch64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_alpine-linux/aqa-tests/TKG/../TKG/output_17164301966600/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-05-23T03:16:33.438Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_alpine-linux/aqa-tests/TKG/../TKG/output_17164301966600/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-05-23T03:16:33.438Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-05-23T03:16:33.438Z] echo "Nothing to be done for teardown."; \
[2024-05-23T03:16:33.438Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_alpine-linux/aqa-tests/TKG/../TKG/output_17164301966600/TestTargetResult";
[2024-05-23T03:16:33.438Z]
[2024-05-23T03:16:33.438Z] TEST SETUP:
[2024-05-23T03:16:33.438Z] Nothing to be done for setup.
[2024-05-23T03:16:33.438Z]
[2024-05-23T03:16:33.438Z] TESTING:
[2024-05-23T03:16:45.953Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-05-23T03:16:54.121Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-05-23T03:17:16.327Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-05-23T03:17:17.962Z] Training: 60056, validation: 20285, test: 19854
[2024-05-23T03:17:17.962Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-05-23T03:17:18.728Z] GC before operation: completed in 444.737 ms, heap usage 51.280 MB -> 37.192 MB.
[2024-05-23T03:18:07.486Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:18:27.086Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:18:48.946Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:19:07.691Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:19:15.806Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:19:25.473Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:19:36.179Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:19:42.839Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:19:43.577Z] 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.
[2024-05-23T03:19:44.342Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:19:45.086Z] Movies recommended for you:
[2024-05-23T03:19:45.086Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:19:45.086Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:19:45.086Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (146625.467 ms) ======
[2024-05-23T03:19:45.086Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-05-23T03:19:45.829Z] GC before operation: completed in 619.089 ms, heap usage 202.383 MB -> 52.531 MB.
[2024-05-23T03:20:01.577Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:20:15.010Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:20:30.903Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:20:42.672Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:20:51.285Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:20:57.934Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:21:06.191Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:21:12.914Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:21:14.454Z] 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.
[2024-05-23T03:21:14.454Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:21:14.454Z] Movies recommended for you:
[2024-05-23T03:21:14.454Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:21:14.454Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:21:14.454Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (89066.325 ms) ======
[2024-05-23T03:21:14.454Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-05-23T03:21:15.204Z] GC before operation: completed in 424.702 ms, heap usage 204.538 MB -> 49.758 MB.
[2024-05-23T03:21:28.594Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:21:41.998Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:21:54.070Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:22:07.556Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:22:12.964Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:22:20.987Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:22:27.640Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:22:34.282Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:22:35.022Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-05-23T03:22:35.022Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:22:35.766Z] Movies recommended for you:
[2024-05-23T03:22:35.766Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:22:35.766Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:22:35.766Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (80711.555 ms) ======
[2024-05-23T03:22:35.766Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-05-23T03:22:36.524Z] GC before operation: completed in 552.237 ms, heap usage 207.337 MB -> 49.947 MB.
[2024-05-23T03:22:51.092Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:23:02.492Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:23:16.037Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:23:27.388Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:23:35.589Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:23:42.378Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:23:49.120Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:23:56.584Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:23:58.185Z] 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.
[2024-05-23T03:23:58.185Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:23:58.185Z] Movies recommended for you:
[2024-05-23T03:23:58.185Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:23:58.185Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:23:58.185Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (81836.828 ms) ======
[2024-05-23T03:23:58.185Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-05-23T03:23:58.935Z] GC before operation: completed in 468.831 ms, heap usage 284.369 MB -> 50.389 MB.
[2024-05-23T03:24:12.525Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:24:26.038Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:24:37.514Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:24:47.311Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:24:55.526Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:25:02.109Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:25:08.847Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:25:16.970Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:25:16.970Z] 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.
[2024-05-23T03:25:17.708Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:25:17.708Z] Movies recommended for you:
[2024-05-23T03:25:17.708Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:25:17.708Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:25:17.708Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (79159.197 ms) ======
[2024-05-23T03:25:17.708Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-05-23T03:25:18.488Z] GC before operation: completed in 465.005 ms, heap usage 176.811 MB -> 50.528 MB.
[2024-05-23T03:25:30.083Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:25:39.805Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:25:53.464Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:26:02.539Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:26:09.312Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:26:17.476Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:26:24.189Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:26:30.943Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:26:32.479Z] 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.
[2024-05-23T03:26:32.480Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:26:33.223Z] Movies recommended for you:
[2024-05-23T03:26:33.223Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:26:33.223Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:26:33.223Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (74732.643 ms) ======
[2024-05-23T03:26:33.223Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-05-23T03:26:33.223Z] GC before operation: completed in 467.614 ms, heap usage 179.502 MB -> 50.444 MB.
[2024-05-23T03:26:44.642Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:26:56.044Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:27:06.595Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:27:18.117Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:27:24.830Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:27:30.246Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:27:37.089Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:27:43.927Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:27:44.682Z] 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.
[2024-05-23T03:27:45.422Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:27:45.422Z] Movies recommended for you:
[2024-05-23T03:27:45.422Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:27:45.422Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:27:45.422Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (72149.469 ms) ======
[2024-05-23T03:27:45.422Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-05-23T03:27:46.163Z] GC before operation: completed in 418.676 ms, heap usage 178.587 MB -> 50.624 MB.
[2024-05-23T03:27:57.631Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:28:08.235Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:28:19.730Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:28:31.114Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:28:37.853Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:28:44.656Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:28:51.376Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:28:58.065Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:28:58.850Z] 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.
[2024-05-23T03:28:58.850Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:28:59.588Z] Movies recommended for you:
[2024-05-23T03:28:59.588Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:28:59.588Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:28:59.588Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (73418.268 ms) ======
[2024-05-23T03:28:59.588Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-05-23T03:28:59.588Z] GC before operation: completed in 430.812 ms, heap usage 178.937 MB -> 50.905 MB.
[2024-05-23T03:29:11.825Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:29:23.298Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:29:34.791Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:29:44.507Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:29:52.548Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:29:59.204Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:30:08.208Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:30:13.620Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:30:15.145Z] 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.
[2024-05-23T03:30:15.145Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:30:15.921Z] Movies recommended for you:
[2024-05-23T03:30:15.921Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:30:15.921Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:30:15.921Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (75855.892 ms) ======
[2024-05-23T03:30:15.921Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-05-23T03:30:16.659Z] GC before operation: completed in 500.435 ms, heap usage 244.484 MB -> 50.739 MB.
[2024-05-23T03:30:28.208Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:30:39.567Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:30:52.951Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:31:02.591Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:31:09.825Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:31:16.458Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:31:24.477Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:31:32.498Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:31:33.245Z] 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.
[2024-05-23T03:31:33.245Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:31:33.245Z] Movies recommended for you:
[2024-05-23T03:31:33.245Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:31:33.245Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:31:33.245Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (77221.467 ms) ======
[2024-05-23T03:31:33.245Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-05-23T03:31:33.990Z] GC before operation: completed in 515.525 ms, heap usage 149.081 MB -> 50.811 MB.
[2024-05-23T03:31:47.402Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:31:57.085Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:32:09.401Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:32:20.759Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:32:27.421Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:32:34.070Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:32:42.098Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:32:48.757Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:32:50.285Z] 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.
[2024-05-23T03:32:50.285Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:32:51.050Z] Movies recommended for you:
[2024-05-23T03:32:51.050Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:32:51.050Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:32:51.050Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (76804.057 ms) ======
[2024-05-23T03:32:51.050Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-05-23T03:32:51.050Z] GC before operation: completed in 522.722 ms, heap usage 226.807 MB -> 50.667 MB.
[2024-05-23T03:33:04.455Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:33:16.821Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:33:28.199Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:33:41.612Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:33:47.024Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:33:55.052Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:34:03.089Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:34:09.749Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:34:10.485Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-05-23T03:34:10.485Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:34:11.974Z] Movies recommended for you:
[2024-05-23T03:34:11.974Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:34:11.974Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:34:11.974Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (79778.927 ms) ======
[2024-05-23T03:34:11.974Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-05-23T03:34:11.974Z] GC before operation: completed in 420.616 ms, heap usage 178.231 MB -> 50.823 MB.
[2024-05-23T03:34:23.342Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:34:34.717Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:34:46.089Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:34:57.491Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:35:04.150Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:35:10.797Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:35:18.424Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:35:25.269Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:35:26.798Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-05-23T03:35:26.798Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:35:27.561Z] Movies recommended for you:
[2024-05-23T03:35:27.561Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:35:27.561Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:35:27.561Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (75760.721 ms) ======
[2024-05-23T03:35:27.561Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-05-23T03:35:27.561Z] GC before operation: completed in 479.198 ms, heap usage 266.688 MB -> 51.054 MB.
[2024-05-23T03:35:38.925Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:35:48.544Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:35:58.138Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:36:07.954Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:36:15.558Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:36:21.080Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:36:26.490Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:36:31.914Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:36:33.450Z] 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.
[2024-05-23T03:36:33.450Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:36:34.198Z] Movies recommended for you:
[2024-05-23T03:36:34.198Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:36:34.198Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:36:34.198Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (66181.339 ms) ======
[2024-05-23T03:36:34.198Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-05-23T03:36:34.198Z] GC before operation: completed in 421.108 ms, heap usage 148.587 MB -> 50.676 MB.
[2024-05-23T03:36:45.578Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:36:55.658Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:37:07.172Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:37:16.219Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:37:22.873Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:37:28.283Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:37:33.713Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:37:38.012Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:37:39.546Z] 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.
[2024-05-23T03:37:39.546Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:37:39.546Z] Movies recommended for you:
[2024-05-23T03:37:39.546Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:37:39.546Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:37:39.546Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (65388.233 ms) ======
[2024-05-23T03:37:39.546Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-05-23T03:37:40.283Z] GC before operation: completed in 516.341 ms, heap usage 112.453 MB -> 52.586 MB.
[2024-05-23T03:37:49.898Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:37:57.922Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:38:08.008Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:38:17.607Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:38:23.076Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:38:28.521Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:38:33.975Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:38:38.984Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:38:39.739Z] 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.
[2024-05-23T03:38:39.739Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:38:40.526Z] Movies recommended for you:
[2024-05-23T03:38:40.526Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:38:40.526Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:38:40.526Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (60066.263 ms) ======
[2024-05-23T03:38:40.526Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-05-23T03:38:40.526Z] GC before operation: completed in 473.961 ms, heap usage 433.348 MB -> 54.445 MB.
[2024-05-23T03:38:52.103Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:39:01.781Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:39:13.296Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:39:22.961Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:39:29.648Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:39:35.153Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:39:40.613Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:39:46.066Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:39:46.837Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-05-23T03:39:46.837Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:39:46.837Z] Movies recommended for you:
[2024-05-23T03:39:46.837Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:39:46.837Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:39:46.837Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (66225.773 ms) ======
[2024-05-23T03:39:46.837Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-05-23T03:39:47.616Z] GC before operation: completed in 510.689 ms, heap usage 424.198 MB -> 54.162 MB.
[2024-05-23T03:39:58.226Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:40:08.024Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:40:17.726Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:40:24.556Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:40:30.112Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:40:34.501Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:40:40.008Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:40:44.392Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:40:45.873Z] 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.
[2024-05-23T03:40:45.873Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:40:45.873Z] Movies recommended for you:
[2024-05-23T03:40:45.873Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:40:45.873Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:40:45.873Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (58413.226 ms) ======
[2024-05-23T03:40:45.873Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-05-23T03:40:46.618Z] GC before operation: completed in 371.566 ms, heap usage 435.875 MB -> 54.196 MB.
[2024-05-23T03:40:56.347Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:41:06.077Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:41:15.845Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:41:24.022Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:41:30.814Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:41:35.366Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:41:42.128Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:41:48.464Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:41:49.223Z] 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.
[2024-05-23T03:41:49.223Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:41:49.223Z] Movies recommended for you:
[2024-05-23T03:41:49.223Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:41:49.223Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:41:49.223Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (62816.594 ms) ======
[2024-05-23T03:41:49.223Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-05-23T03:41:49.223Z] GC before operation: completed in 372.406 ms, heap usage 206.241 MB -> 49.481 MB.
[2024-05-23T03:41:58.974Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-23T03:42:07.144Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-23T03:42:16.910Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-23T03:42:25.065Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-23T03:42:29.420Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-23T03:42:34.889Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-23T03:42:41.608Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-23T03:42:46.756Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-23T03:42:48.316Z] 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.
[2024-05-23T03:42:48.316Z] The best model improves the baseline by 14.52%.
[2024-05-23T03:42:48.316Z] Movies recommended for you:
[2024-05-23T03:42:48.316Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-23T03:42:48.316Z] There is no way to check that no silent failure occurred.
[2024-05-23T03:42:48.316Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (58961.680 ms) ======
[2024-05-23T03:42:52.794Z] -----------------------------------
[2024-05-23T03:42:52.794Z] renaissance-movie-lens_0_PASSED
[2024-05-23T03:42:52.794Z] -----------------------------------
[2024-05-23T03:42:52.794Z]
[2024-05-23T03:42:52.794Z] TEST TEARDOWN:
[2024-05-23T03:42:52.794Z] Nothing to be done for teardown.
[2024-05-23T03:42:52.794Z] renaissance-movie-lens_0 Finish Time: Thu May 23 03:42:52 2024 Epoch Time (ms): 1716435772285