renaissance-movie-lens_0
[2023-04-18T22:15:59.594Z] Running test renaissance-movie-lens_0 ...
[2023-04-18T22:15:59.594Z] ===============================================
[2023-04-18T22:15:59.594Z] renaissance-movie-lens_0 Start Time: Tue Apr 18 22:15:59 2023 Epoch Time (ms): 1681856159551
[2023-04-18T22:15:59.594Z] variation: NoOptions
[2023-04-18T22:15:59.594Z] JVM_OPTIONS:
[2023-04-18T22:15:59.594Z] { \
[2023-04-18T22:15:59.594Z] echo ""; echo "TEST SETUP:"; \
[2023-04-18T22:15:59.594Z] echo "Nothing to be done for setup."; \
[2023-04-18T22:15:59.594Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1681855131661/renaissance-movie-lens_0"; \
[2023-04-18T22:15:59.594Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1681855131661/renaissance-movie-lens_0"; \
[2023-04-18T22:15:59.594Z] echo ""; echo "TESTING:"; \
[2023-04-18T22:15:59.594Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/openjdkbinary/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_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1681855131661/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-18T22:15:59.594Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1681855131661/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-18T22:15:59.594Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-18T22:15:59.594Z] echo "Nothing to be done for teardown."; \
[2023-04-18T22:15:59.594Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1681855131661/TestTargetResult";
[2023-04-18T22:15:59.594Z]
[2023-04-18T22:15:59.594Z] TEST SETUP:
[2023-04-18T22:15:59.594Z] Nothing to be done for setup.
[2023-04-18T22:15:59.594Z]
[2023-04-18T22:15:59.594Z] TESTING:
[2023-04-18T22:16:03.689Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-18T22:16:06.935Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2023-04-18T22:16:12.440Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-18T22:16:12.440Z] Training: 60056, validation: 20285, test: 19854
[2023-04-18T22:16:12.440Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-18T22:16:12.440Z] GC before operation: completed in 60.348 ms, heap usage 92.858 MB -> 36.803 MB.
[2023-04-18T22:16:22.085Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:16:26.340Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:16:30.579Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:16:34.781Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:16:36.660Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:16:38.550Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:16:41.115Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:16:43.004Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:16:43.419Z] 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.
[2023-04-18T22:16:43.419Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:16:43.419Z] Movies recommended for you:
[2023-04-18T22:16:43.419Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:16:43.419Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:16:43.419Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (31356.938 ms) ======
[2023-04-18T22:16:43.419Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-18T22:16:43.812Z] GC before operation: completed in 83.281 ms, heap usage 232.911 MB -> 55.107 MB.
[2023-04-18T22:16:47.239Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:16:50.596Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:16:53.886Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:16:57.189Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:16:58.499Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:17:00.377Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:17:02.218Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:17:04.110Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:17:04.500Z] 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.
[2023-04-18T22:17:04.500Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:17:04.500Z] Movies recommended for you:
[2023-04-18T22:17:04.500Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:17:04.500Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:17:04.500Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20955.372 ms) ======
[2023-04-18T22:17:04.500Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-18T22:17:04.873Z] GC before operation: completed in 76.269 ms, heap usage 323.229 MB -> 49.467 MB.
[2023-04-18T22:17:07.410Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:17:11.562Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:17:14.232Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:17:16.729Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:17:18.621Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:17:20.525Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:17:22.440Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:17:24.314Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:17:24.701Z] 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.
[2023-04-18T22:17:24.701Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:17:24.701Z] Movies recommended for you:
[2023-04-18T22:17:24.701Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:17:24.701Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:17:24.701Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20098.081 ms) ======
[2023-04-18T22:17:24.701Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-18T22:17:24.701Z] GC before operation: completed in 76.715 ms, heap usage 281.360 MB -> 49.750 MB.
[2023-04-18T22:17:27.993Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:17:30.548Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:17:33.804Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:17:36.322Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:17:38.192Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:17:39.491Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:17:41.535Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:17:42.924Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:17:43.319Z] 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.
[2023-04-18T22:17:43.319Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:17:43.319Z] Movies recommended for you:
[2023-04-18T22:17:43.319Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:17:43.319Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:17:43.319Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18497.828 ms) ======
[2023-04-18T22:17:43.319Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-18T22:17:43.319Z] GC before operation: completed in 79.127 ms, heap usage 90.624 MB -> 50.046 MB.
[2023-04-18T22:17:46.609Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:17:49.148Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:17:52.434Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:17:54.952Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:17:56.825Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:17:58.761Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:18:00.111Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:18:02.026Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:18:02.437Z] 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.
[2023-04-18T22:18:02.437Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:18:02.437Z] Movies recommended for you:
[2023-04-18T22:18:02.437Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:18:02.437Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:18:02.437Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (19138.358 ms) ======
[2023-04-18T22:18:02.437Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-18T22:18:02.823Z] GC before operation: completed in 74.431 ms, heap usage 171.529 MB -> 50.301 MB.
[2023-04-18T22:18:05.353Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:18:08.634Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:18:11.339Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:18:13.833Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:18:15.743Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:18:17.102Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:18:18.964Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:18:20.856Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:18:20.857Z] 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.
[2023-04-18T22:18:20.857Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:18:21.235Z] Movies recommended for you:
[2023-04-18T22:18:21.235Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:18:21.235Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:18:21.235Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18420.906 ms) ======
[2023-04-18T22:18:21.235Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-18T22:18:21.235Z] GC before operation: completed in 79.694 ms, heap usage 352.754 MB -> 50.401 MB.
[2023-04-18T22:18:23.737Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:18:27.054Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:18:29.573Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:18:32.130Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:18:34.032Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:18:35.903Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:18:37.424Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:18:38.758Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:18:39.165Z] 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.
[2023-04-18T22:18:39.165Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:18:39.165Z] Movies recommended for you:
[2023-04-18T22:18:39.165Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:18:39.165Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:18:39.165Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (18149.651 ms) ======
[2023-04-18T22:18:39.165Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-18T22:18:39.537Z] GC before operation: completed in 77.304 ms, heap usage 232.699 MB -> 50.402 MB.
[2023-04-18T22:18:42.074Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:18:44.599Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:18:47.091Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:18:49.616Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:18:51.482Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:18:52.791Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:18:54.710Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:18:56.056Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:18:56.476Z] 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.
[2023-04-18T22:18:56.476Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:18:56.476Z] Movies recommended for you:
[2023-04-18T22:18:56.476Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:18:56.476Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:18:56.476Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17207.454 ms) ======
[2023-04-18T22:18:56.476Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-18T22:18:56.849Z] GC before operation: completed in 74.260 ms, heap usage 132.406 MB -> 50.631 MB.
[2023-04-18T22:18:59.379Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:19:01.909Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:19:04.433Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:19:06.936Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:19:08.824Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:19:10.123Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:19:12.014Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:19:13.329Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:19:13.712Z] 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.
[2023-04-18T22:19:13.712Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:19:13.712Z] Movies recommended for you:
[2023-04-18T22:19:13.712Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:19:13.712Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:19:13.712Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17047.197 ms) ======
[2023-04-18T22:19:13.712Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-18T22:19:13.712Z] GC before operation: completed in 78.597 ms, heap usage 129.970 MB -> 50.509 MB.
[2023-04-18T22:19:16.225Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:19:18.752Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:19:22.026Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:19:23.890Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:19:25.741Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:19:27.059Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:19:28.936Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:19:31.013Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:19:31.013Z] 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.
[2023-04-18T22:19:31.013Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:19:31.013Z] Movies recommended for you:
[2023-04-18T22:19:31.013Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:19:31.013Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:19:31.013Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17152.695 ms) ======
[2023-04-18T22:19:31.013Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-18T22:19:31.013Z] GC before operation: completed in 84.372 ms, heap usage 234.798 MB -> 50.640 MB.
[2023-04-18T22:19:33.565Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:19:36.095Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:19:38.592Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:19:41.882Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:19:43.176Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:19:45.061Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:19:46.361Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:19:48.259Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:19:48.259Z] 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.
[2023-04-18T22:19:48.259Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:19:48.642Z] Movies recommended for you:
[2023-04-18T22:19:48.642Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:19:48.642Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:19:48.642Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17425.405 ms) ======
[2023-04-18T22:19:48.642Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-18T22:19:48.642Z] GC before operation: completed in 77.987 ms, heap usage 132.798 MB -> 50.272 MB.
[2023-04-18T22:19:51.155Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:19:53.689Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:19:56.186Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:19:58.696Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:20:00.661Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:20:01.999Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:20:03.883Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:20:05.239Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:20:05.616Z] 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.
[2023-04-18T22:20:05.616Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:20:05.616Z] Movies recommended for you:
[2023-04-18T22:20:05.616Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:20:05.616Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:20:05.616Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17081.718 ms) ======
[2023-04-18T22:20:05.616Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-18T22:20:05.616Z] GC before operation: completed in 79.865 ms, heap usage 230.351 MB -> 50.526 MB.
[2023-04-18T22:20:08.133Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:20:11.400Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:20:13.955Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:20:15.861Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:20:17.749Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:20:19.050Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:20:20.921Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:20:22.790Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:20:23.170Z] 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.
[2023-04-18T22:20:23.170Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:20:23.170Z] Movies recommended for you:
[2023-04-18T22:20:23.170Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:20:23.170Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:20:23.170Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17449.757 ms) ======
[2023-04-18T22:20:23.170Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-18T22:20:23.170Z] GC before operation: completed in 77.621 ms, heap usage 273.117 MB -> 50.812 MB.
[2023-04-18T22:20:25.819Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:20:28.309Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:20:31.629Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:20:33.483Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:20:35.329Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:20:36.630Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:20:38.508Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:20:40.376Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:20:40.376Z] 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.
[2023-04-18T22:20:40.376Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:20:40.376Z] Movies recommended for you:
[2023-04-18T22:20:40.376Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:20:40.376Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:20:40.376Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17264.613 ms) ======
[2023-04-18T22:20:40.376Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-18T22:20:40.748Z] GC before operation: completed in 74.873 ms, heap usage 192.369 MB -> 50.467 MB.
[2023-04-18T22:20:43.234Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:20:45.759Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:20:48.279Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:20:50.776Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:20:52.693Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:20:53.823Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:20:55.691Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:20:57.020Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:20:57.395Z] 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.
[2023-04-18T22:20:57.395Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:20:57.780Z] Movies recommended for you:
[2023-04-18T22:20:57.780Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:20:57.780Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:20:57.780Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (17063.135 ms) ======
[2023-04-18T22:20:57.780Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-18T22:20:57.780Z] GC before operation: completed in 77.455 ms, heap usage 288.165 MB -> 50.685 MB.
[2023-04-18T22:21:00.309Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:21:02.815Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:21:05.361Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:21:07.890Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:21:09.780Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:21:11.084Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:21:12.966Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:21:14.849Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:21:14.849Z] 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.
[2023-04-18T22:21:14.849Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:21:15.229Z] Movies recommended for you:
[2023-04-18T22:21:15.229Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:21:15.229Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:21:15.229Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17323.699 ms) ======
[2023-04-18T22:21:15.229Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-18T22:21:15.229Z] GC before operation: completed in 82.447 ms, heap usage 332.940 MB -> 50.845 MB.
[2023-04-18T22:21:17.751Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:21:20.265Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:21:23.558Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:21:25.424Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:21:27.313Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:21:28.615Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:21:30.486Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:21:31.819Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:21:32.197Z] 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.
[2023-04-18T22:21:32.197Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:21:32.197Z] Movies recommended for you:
[2023-04-18T22:21:32.197Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:21:32.197Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:21:32.197Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17150.651 ms) ======
[2023-04-18T22:21:32.197Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-18T22:21:32.197Z] GC before operation: completed in 81.920 ms, heap usage 226.255 MB -> 50.586 MB.
[2023-04-18T22:21:34.720Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:21:37.999Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:21:40.511Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:21:42.389Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:21:44.252Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:21:45.580Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:21:47.604Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:21:48.907Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:21:49.298Z] 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.
[2023-04-18T22:21:49.298Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:21:49.298Z] Movies recommended for you:
[2023-04-18T22:21:49.298Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:21:49.298Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:21:49.298Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (17009.591 ms) ======
[2023-04-18T22:21:49.298Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-18T22:21:49.298Z] GC before operation: completed in 78.367 ms, heap usage 115.911 MB -> 51.310 MB.
[2023-04-18T22:21:51.814Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:21:54.337Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:21:57.647Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:22:00.159Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:22:01.491Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:22:02.782Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:22:04.663Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:22:06.003Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:22:06.398Z] 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.
[2023-04-18T22:22:06.399Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:22:06.783Z] Movies recommended for you:
[2023-04-18T22:22:06.783Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:22:06.783Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:22:06.783Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (17179.378 ms) ======
[2023-04-18T22:22:06.783Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-18T22:22:06.783Z] GC before operation: completed in 78.820 ms, heap usage 178.161 MB -> 50.847 MB.
[2023-04-18T22:22:09.304Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:22:11.816Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:22:14.483Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:22:17.032Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:22:18.908Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:22:20.272Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:22:21.626Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:22:23.522Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:22:23.522Z] 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.
[2023-04-18T22:22:23.895Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:22:23.895Z] Movies recommended for you:
[2023-04-18T22:22:23.895Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:22:23.895Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:22:23.895Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (17140.795 ms) ======
[2023-04-18T22:22:24.694Z] -----------------------------------
[2023-04-18T22:22:24.694Z] renaissance-movie-lens_0_PASSED
[2023-04-18T22:22:24.694Z] -----------------------------------
[2023-04-18T22:22:24.694Z]
[2023-04-18T22:22:24.694Z] TEST TEARDOWN:
[2023-04-18T22:22:24.694Z] Nothing to be done for teardown.
[2023-04-18T22:22:24.694Z] renaissance-movie-lens_0 Finish Time: Tue Apr 18 22:22:24 2023 Epoch Time (ms): 1681856544316