renaissance-movie-lens_0
[2025-02-06T00:02:36.780Z] Running test renaissance-movie-lens_0 ...
[2025-02-06T00:02:36.780Z] ===============================================
[2025-02-06T00:02:36.780Z] renaissance-movie-lens_0 Start Time: Thu Feb 6 00:02:35 2025 Epoch Time (ms): 1738800155925
[2025-02-06T00:02:36.780Z] variation: NoOptions
[2025-02-06T00:02:36.780Z] JVM_OPTIONS:
[2025-02-06T00:02:36.780Z] { \
[2025-02-06T00:02:36.780Z] echo ""; echo "TEST SETUP:"; \
[2025-02-06T00:02:36.780Z] echo "Nothing to be done for setup."; \
[2025-02-06T00:02:36.780Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387991961701/renaissance-movie-lens_0"; \
[2025-02-06T00:02:36.781Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387991961701/renaissance-movie-lens_0"; \
[2025-02-06T00:02:36.781Z] echo ""; echo "TESTING:"; \
[2025-02-06T00:02:36.781Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387991961701/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-06T00:02:36.781Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387991961701/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-06T00:02:36.781Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-06T00:02:36.781Z] echo "Nothing to be done for teardown."; \
[2025-02-06T00:02:36.781Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17387991961701/TestTargetResult";
[2025-02-06T00:02:36.781Z]
[2025-02-06T00:02:36.781Z] TEST SETUP:
[2025-02-06T00:02:36.781Z] Nothing to be done for setup.
[2025-02-06T00:02:36.781Z]
[2025-02-06T00:02:36.781Z] TESTING:
[2025-02-06T00:02:39.800Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-06T00:02:41.861Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-06T00:02:44.885Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-06T00:02:44.885Z] Training: 60056, validation: 20285, test: 19854
[2025-02-06T00:02:44.885Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-06T00:02:44.885Z] GC before operation: completed in 78.259 ms, heap usage 50.002 MB -> 36.493 MB.
[2025-02-06T00:02:50.277Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:02:54.424Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:02:57.449Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:02:59.405Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:03:01.362Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:03:03.330Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:03:04.280Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:03:06.237Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:03:06.237Z] 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.
[2025-02-06T00:03:06.237Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:03:06.237Z] Movies recommended for you:
[2025-02-06T00:03:06.237Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:03:06.237Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:03:06.237Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21248.683 ms) ======
[2025-02-06T00:03:06.237Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-06T00:03:06.237Z] GC before operation: completed in 91.975 ms, heap usage 313.466 MB -> 48.270 MB.
[2025-02-06T00:03:09.254Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:03:11.225Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:03:14.244Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:03:16.203Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:03:18.159Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:03:20.116Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:03:21.074Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:03:23.047Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:03:23.047Z] 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.
[2025-02-06T00:03:23.047Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:03:23.047Z] Movies recommended for you:
[2025-02-06T00:03:23.047Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:03:23.047Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:03:23.047Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16524.450 ms) ======
[2025-02-06T00:03:23.047Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-06T00:03:23.047Z] GC before operation: completed in 101.716 ms, heap usage 285.388 MB -> 49.098 MB.
[2025-02-06T00:03:25.010Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:03:28.035Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:03:29.988Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:03:34.449Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:03:34.449Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:03:35.401Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:03:36.355Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:03:37.305Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:03:38.259Z] 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.
[2025-02-06T00:03:38.259Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:03:38.259Z] Movies recommended for you:
[2025-02-06T00:03:38.259Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:03:38.259Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:03:38.259Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14811.437 ms) ======
[2025-02-06T00:03:38.259Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-06T00:03:38.259Z] GC before operation: completed in 90.053 ms, heap usage 324.229 MB -> 49.488 MB.
[2025-02-06T00:03:40.214Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:03:43.254Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:03:45.210Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:03:47.164Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:03:48.117Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:03:50.075Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:03:51.195Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:03:53.154Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:03:53.154Z] 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.
[2025-02-06T00:03:53.154Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:03:53.154Z] Movies recommended for you:
[2025-02-06T00:03:53.154Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:03:53.154Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:03:53.154Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14951.826 ms) ======
[2025-02-06T00:03:53.154Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-06T00:03:53.154Z] GC before operation: completed in 91.972 ms, heap usage 172.702 MB -> 49.658 MB.
[2025-02-06T00:03:56.207Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:03:58.160Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:04:00.114Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:04:03.135Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:04:04.086Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:04:05.038Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:04:06.994Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:04:07.947Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:04:08.899Z] 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.
[2025-02-06T00:04:08.899Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:04:08.899Z] Movies recommended for you:
[2025-02-06T00:04:08.899Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:04:08.899Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:04:08.899Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15527.367 ms) ======
[2025-02-06T00:04:08.899Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-06T00:04:08.899Z] GC before operation: completed in 80.792 ms, heap usage 123.380 MB -> 49.802 MB.
[2025-02-06T00:04:10.855Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:04:12.820Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:04:14.774Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:04:16.737Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:04:18.690Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:04:19.643Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:04:20.594Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:04:22.564Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:04:22.564Z] 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.
[2025-02-06T00:04:22.564Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:04:22.564Z] Movies recommended for you:
[2025-02-06T00:04:22.564Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:04:22.564Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:04:22.564Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13921.823 ms) ======
[2025-02-06T00:04:22.564Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-06T00:04:22.564Z] GC before operation: completed in 86.697 ms, heap usage 64.307 MB -> 49.619 MB.
[2025-02-06T00:04:25.584Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:04:27.539Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:04:29.497Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:04:31.461Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:04:32.425Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:04:34.385Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:04:37.358Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:04:37.358Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:04:37.358Z] 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.
[2025-02-06T00:04:37.358Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:04:37.358Z] Movies recommended for you:
[2025-02-06T00:04:37.358Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:04:37.358Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:04:37.358Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14322.862 ms) ======
[2025-02-06T00:04:37.358Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-06T00:04:37.358Z] GC before operation: completed in 92.002 ms, heap usage 362.920 MB -> 50.118 MB.
[2025-02-06T00:04:39.310Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:04:41.272Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:04:44.286Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:04:46.247Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:04:47.203Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:04:48.164Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:04:50.117Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:04:51.068Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:04:51.068Z] 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.
[2025-02-06T00:04:51.068Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:04:51.068Z] Movies recommended for you:
[2025-02-06T00:04:51.068Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:04:51.068Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:04:51.068Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14325.331 ms) ======
[2025-02-06T00:04:51.068Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-06T00:04:52.019Z] GC before operation: completed in 85.205 ms, heap usage 101.953 MB -> 50.098 MB.
[2025-02-06T00:04:53.972Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:04:55.926Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:04:57.907Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:04:59.859Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:05:00.810Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:05:02.767Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:05:03.718Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:05:04.671Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:05:05.622Z] 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.
[2025-02-06T00:05:05.622Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:05:05.622Z] Movies recommended for you:
[2025-02-06T00:05:05.622Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:05:05.622Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:05:05.622Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13937.255 ms) ======
[2025-02-06T00:05:05.622Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-06T00:05:05.622Z] GC before operation: completed in 97.789 ms, heap usage 263.059 MB -> 50.124 MB.
[2025-02-06T00:05:07.576Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:05:10.597Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:05:12.553Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:05:14.510Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:05:15.535Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:05:16.496Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:05:18.454Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:05:19.406Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:05:19.406Z] 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.
[2025-02-06T00:05:19.406Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:05:19.406Z] Movies recommended for you:
[2025-02-06T00:05:19.406Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:05:19.406Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:05:19.406Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14202.995 ms) ======
[2025-02-06T00:05:19.406Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-06T00:05:20.358Z] GC before operation: completed in 86.057 ms, heap usage 174.847 MB -> 50.203 MB.
[2025-02-06T00:05:22.311Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:05:24.264Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:05:26.219Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:05:28.181Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:05:29.133Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:05:30.086Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:05:32.039Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:05:32.994Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:05:32.994Z] 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.
[2025-02-06T00:05:32.994Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:05:32.994Z] Movies recommended for you:
[2025-02-06T00:05:32.994Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:05:32.994Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:05:32.994Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13476.440 ms) ======
[2025-02-06T00:05:32.994Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-06T00:05:33.945Z] GC before operation: completed in 80.291 ms, heap usage 234.493 MB -> 49.888 MB.
[2025-02-06T00:05:35.901Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:05:37.861Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:05:39.812Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:05:43.804Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:05:43.804Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:05:43.804Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:05:45.770Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:05:46.743Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:05:46.743Z] 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.
[2025-02-06T00:05:46.743Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:05:46.743Z] Movies recommended for you:
[2025-02-06T00:05:46.743Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:05:46.743Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:05:46.743Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13638.389 ms) ======
[2025-02-06T00:05:46.743Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-06T00:05:47.703Z] GC before operation: completed in 86.120 ms, heap usage 391.798 MB -> 53.475 MB.
[2025-02-06T00:05:49.658Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:05:51.627Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:05:53.585Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:05:55.550Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:05:57.506Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:05:58.459Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:06:00.414Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:06:01.366Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:06:01.366Z] 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.
[2025-02-06T00:06:01.366Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:06:02.333Z] Movies recommended for you:
[2025-02-06T00:06:02.333Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:06:02.333Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:06:02.333Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14628.514 ms) ======
[2025-02-06T00:06:02.333Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-06T00:06:02.333Z] GC before operation: completed in 85.765 ms, heap usage 270.573 MB -> 50.278 MB.
[2025-02-06T00:06:04.298Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:06:06.267Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:06:09.278Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:06:11.231Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:06:12.187Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:06:13.139Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:06:15.091Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:06:16.045Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:06:16.045Z] 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.
[2025-02-06T00:06:16.045Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:06:16.045Z] Movies recommended for you:
[2025-02-06T00:06:16.045Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:06:16.045Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:06:16.045Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14450.548 ms) ======
[2025-02-06T00:06:16.045Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-06T00:06:16.045Z] GC before operation: completed in 97.813 ms, heap usage 252.818 MB -> 50.065 MB.
[2025-02-06T00:06:19.062Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:06:21.020Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:06:22.983Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:06:24.940Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:06:25.890Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:06:27.851Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:06:28.813Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:06:29.770Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:06:29.770Z] 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.
[2025-02-06T00:06:29.770Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:06:30.720Z] Movies recommended for you:
[2025-02-06T00:06:30.720Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:06:30.720Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:06:30.720Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13824.666 ms) ======
[2025-02-06T00:06:30.720Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-06T00:06:30.720Z] GC before operation: completed in 88.373 ms, heap usage 257.459 MB -> 50.274 MB.
[2025-02-06T00:06:32.679Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:06:34.635Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:06:36.608Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:06:38.567Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:06:40.848Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:06:41.800Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:06:42.751Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:06:46.541Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:06:46.541Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-06T00:06:46.541Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:06:46.541Z] Movies recommended for you:
[2025-02-06T00:06:46.541Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:06:46.541Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:06:46.541Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14438.784 ms) ======
[2025-02-06T00:06:46.541Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-06T00:06:46.541Z] GC before operation: completed in 103.427 ms, heap usage 180.388 MB -> 50.243 MB.
[2025-02-06T00:06:46.541Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:06:49.560Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:06:51.522Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:06:53.476Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:06:54.428Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:06:56.383Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:06:57.339Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:06:59.293Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:06:59.293Z] 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.
[2025-02-06T00:06:59.293Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:06:59.293Z] Movies recommended for you:
[2025-02-06T00:06:59.293Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:06:59.293Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:06:59.293Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14236.392 ms) ======
[2025-02-06T00:06:59.293Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-06T00:06:59.293Z] GC before operation: completed in 103.647 ms, heap usage 224.402 MB -> 50.122 MB.
[2025-02-06T00:07:01.259Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:07:04.287Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:07:06.255Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:07:08.212Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:07:09.163Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:07:10.157Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:07:12.110Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:07:13.061Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:07:13.061Z] 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.
[2025-02-06T00:07:13.061Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:07:13.061Z] Movies recommended for you:
[2025-02-06T00:07:13.061Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:07:13.061Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:07:13.061Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14165.905 ms) ======
[2025-02-06T00:07:13.061Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-06T00:07:14.013Z] GC before operation: completed in 88.967 ms, heap usage 250.878 MB -> 50.200 MB.
[2025-02-06T00:07:15.974Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:07:17.929Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:07:19.900Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:07:21.855Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:07:22.807Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:07:23.758Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:07:25.720Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:07:26.671Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:07:26.671Z] 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.
[2025-02-06T00:07:26.671Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:07:26.671Z] Movies recommended for you:
[2025-02-06T00:07:26.671Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:07:26.671Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:07:26.671Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (13526.141 ms) ======
[2025-02-06T00:07:26.671Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-06T00:07:26.671Z] GC before operation: completed in 78.514 ms, heap usage 180.315 MB -> 50.353 MB.
[2025-02-06T00:07:29.691Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T00:07:31.651Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T00:07:33.638Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T00:07:35.598Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T00:07:36.549Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T00:07:37.500Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T00:07:39.462Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T00:07:40.412Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T00:07:40.412Z] 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.
[2025-02-06T00:07:40.412Z] The best model improves the baseline by 14.52%.
[2025-02-06T00:07:40.412Z] Movies recommended for you:
[2025-02-06T00:07:40.412Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T00:07:40.412Z] There is no way to check that no silent failure occurred.
[2025-02-06T00:07:40.412Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (13437.421 ms) ======
[2025-02-06T00:07:41.363Z] -----------------------------------
[2025-02-06T00:07:41.363Z] renaissance-movie-lens_0_PASSED
[2025-02-06T00:07:41.363Z] -----------------------------------
[2025-02-06T00:07:41.363Z]
[2025-02-06T00:07:41.363Z] TEST TEARDOWN:
[2025-02-06T00:07:41.363Z] Nothing to be done for teardown.
[2025-02-06T00:07:41.363Z] renaissance-movie-lens_0 Finish Time: Thu Feb 6 00:07:40 2025 Epoch Time (ms): 1738800460539