renaissance-movie-lens_0
[2024-11-22T23:17:21.587Z] Running test renaissance-movie-lens_0 ...
[2024-11-22T23:17:21.587Z] ===============================================
[2024-11-22T23:17:21.587Z] renaissance-movie-lens_0 Start Time: Fri Nov 22 23:17:20 2024 Epoch Time (ms): 1732317440316
[2024-11-22T23:17:21.587Z] variation: NoOptions
[2024-11-22T23:17:21.587Z] JVM_OPTIONS:
[2024-11-22T23:17:21.587Z] { \
[2024-11-22T23:17:21.587Z] echo ""; echo "TEST SETUP:"; \
[2024-11-22T23:17:21.587Z] echo "Nothing to be done for setup."; \
[2024-11-22T23:17:21.587Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17323165998723/renaissance-movie-lens_0"; \
[2024-11-22T23:17:21.587Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17323165998723/renaissance-movie-lens_0"; \
[2024-11-22T23:17:21.587Z] echo ""; echo "TESTING:"; \
[2024-11-22T23:17:21.587Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17323165998723/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-22T23:17:21.587Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17323165998723/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-22T23:17:21.587Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-22T23:17:21.587Z] echo "Nothing to be done for teardown."; \
[2024-11-22T23:17:21.587Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17323165998723/TestTargetResult";
[2024-11-22T23:17:21.587Z]
[2024-11-22T23:17:21.587Z] TEST SETUP:
[2024-11-22T23:17:21.587Z] Nothing to be done for setup.
[2024-11-22T23:17:21.587Z]
[2024-11-22T23:17:21.587Z] TESTING:
[2024-11-22T23:17:23.524Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-22T23:17:26.883Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-22T23:17:28.806Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-22T23:17:28.806Z] Training: 60056, validation: 20285, test: 19854
[2024-11-22T23:17:28.806Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-22T23:17:28.806Z] GC before operation: completed in 54.864 ms, heap usage 128.978 MB -> 37.265 MB.
[2024-11-22T23:17:34.114Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:17:36.899Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:17:38.826Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:17:41.827Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:17:42.765Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:17:44.247Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:17:46.338Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:17:47.273Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:17:49.884Z] 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.
[2024-11-22T23:17:49.884Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:17:49.884Z] Movies recommended for you:
[2024-11-22T23:17:49.884Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:17:49.884Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:17:49.884Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (19566.875 ms) ======
[2024-11-22T23:17:49.884Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-22T23:17:49.884Z] GC before operation: completed in 90.972 ms, heap usage 321.687 MB -> 52.904 MB.
[2024-11-22T23:17:50.990Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:17:52.911Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:17:55.892Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:17:57.809Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:17:58.861Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:17:59.805Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:18:01.734Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:18:03.819Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:18:03.819Z] 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.
[2024-11-22T23:18:03.819Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:18:03.819Z] Movies recommended for you:
[2024-11-22T23:18:03.819Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:18:03.819Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:18:03.819Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15004.214 ms) ======
[2024-11-22T23:18:03.819Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-22T23:18:03.819Z] GC before operation: completed in 71.641 ms, heap usage 323.155 MB -> 49.951 MB.
[2024-11-22T23:18:05.934Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:18:08.945Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:18:09.885Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:18:12.536Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:18:13.669Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:18:14.605Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:18:15.558Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:18:17.492Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:18:17.492Z] 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.
[2024-11-22T23:18:17.492Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:18:17.492Z] Movies recommended for you:
[2024-11-22T23:18:17.492Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:18:17.492Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:18:17.492Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (13999.857 ms) ======
[2024-11-22T23:18:17.492Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-22T23:18:17.492Z] GC before operation: completed in 67.964 ms, heap usage 422.804 MB -> 53.495 MB.
[2024-11-22T23:18:19.451Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:18:21.375Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:18:23.305Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:18:25.403Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:18:27.338Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:18:28.275Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:18:35.719Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:18:35.719Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:18:35.719Z] 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.
[2024-11-22T23:18:35.719Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:18:35.719Z] Movies recommended for you:
[2024-11-22T23:18:35.719Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:18:35.719Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:18:35.719Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13858.807 ms) ======
[2024-11-22T23:18:35.719Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-22T23:18:35.719Z] GC before operation: completed in 73.745 ms, heap usage 275.485 MB -> 50.541 MB.
[2024-11-22T23:18:35.719Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:18:35.719Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:18:37.756Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:18:39.396Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:18:40.504Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:18:42.430Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:18:43.368Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:18:44.327Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:18:45.265Z] 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.
[2024-11-22T23:18:45.265Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:18:45.265Z] Movies recommended for you:
[2024-11-22T23:18:45.265Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:18:45.265Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:18:45.265Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13711.229 ms) ======
[2024-11-22T23:18:45.265Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-22T23:18:45.265Z] GC before operation: completed in 80.165 ms, heap usage 416.342 MB -> 54.141 MB.
[2024-11-22T23:18:47.191Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:18:49.117Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:18:51.048Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:18:52.970Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:18:53.909Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:18:55.840Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:18:56.779Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:18:57.717Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:18:57.717Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-22T23:18:57.717Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:18:57.717Z] Movies recommended for you:
[2024-11-22T23:18:57.717Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:18:57.717Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:18:57.717Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (12924.612 ms) ======
[2024-11-22T23:18:57.717Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-22T23:18:57.717Z] GC before operation: completed in 69.246 ms, heap usage 429.904 MB -> 53.986 MB.
[2024-11-22T23:18:59.641Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:19:01.564Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:19:03.501Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:19:05.430Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:19:07.487Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:19:07.488Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:19:09.411Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:19:10.350Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:19:10.350Z] 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.
[2024-11-22T23:19:10.350Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:19:10.350Z] Movies recommended for you:
[2024-11-22T23:19:10.350Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:19:10.350Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:19:10.350Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (12397.226 ms) ======
[2024-11-22T23:19:10.350Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-22T23:19:10.350Z] GC before operation: completed in 67.257 ms, heap usage 426.729 MB -> 54.210 MB.
[2024-11-22T23:19:12.279Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:19:14.202Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:19:16.126Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:19:18.051Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:19:18.981Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:19:20.624Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:19:21.561Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:19:22.503Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:19:22.503Z] 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.
[2024-11-22T23:19:22.503Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:19:22.503Z] Movies recommended for you:
[2024-11-22T23:19:22.503Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:19:22.503Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:19:22.503Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12099.435 ms) ======
[2024-11-22T23:19:22.503Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-22T23:19:22.503Z] GC before operation: completed in 64.936 ms, heap usage 161.442 MB -> 51.094 MB.
[2024-11-22T23:19:24.426Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:19:26.418Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:19:28.347Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:19:31.458Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:19:31.458Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:19:33.385Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:19:42.106Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:19:42.106Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:19:42.106Z] 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.
[2024-11-22T23:19:42.106Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:19:42.106Z] Movies recommended for you:
[2024-11-22T23:19:42.106Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:19:42.106Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:19:42.106Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13052.865 ms) ======
[2024-11-22T23:19:42.106Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-22T23:19:42.106Z] GC before operation: completed in 89.405 ms, heap usage 404.666 MB -> 54.273 MB.
[2024-11-22T23:19:42.106Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:19:42.106Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:19:42.106Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:19:44.269Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:19:45.218Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:19:46.156Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:19:47.090Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:19:48.740Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:19:48.740Z] 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.
[2024-11-22T23:19:48.740Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:19:48.740Z] Movies recommended for you:
[2024-11-22T23:19:48.740Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:19:48.740Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:19:48.740Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13092.272 ms) ======
[2024-11-22T23:19:48.740Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-22T23:19:48.740Z] GC before operation: completed in 76.457 ms, heap usage 437.405 MB -> 57.698 MB.
[2024-11-22T23:19:50.685Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:19:52.871Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:19:55.015Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:19:56.943Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:19:57.885Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:19:58.822Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:20:00.745Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:20:01.684Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:20:01.684Z] 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.
[2024-11-22T23:20:01.684Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:20:01.684Z] Movies recommended for you:
[2024-11-22T23:20:01.684Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:20:01.684Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:20:01.684Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12785.567 ms) ======
[2024-11-22T23:20:01.684Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-22T23:20:01.684Z] GC before operation: completed in 75.032 ms, heap usage 284.398 MB -> 50.828 MB.
[2024-11-22T23:20:03.608Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:20:05.522Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:20:07.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:20:09.372Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:20:10.307Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:20:12.227Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:20:13.165Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:20:14.102Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:20:14.102Z] 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.
[2024-11-22T23:20:14.102Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:20:14.102Z] Movies recommended for you:
[2024-11-22T23:20:14.102Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:20:14.102Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:20:14.102Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12499.771 ms) ======
[2024-11-22T23:20:14.102Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-22T23:20:14.102Z] GC before operation: completed in 67.051 ms, heap usage 181.381 MB -> 51.044 MB.
[2024-11-22T23:20:16.026Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:20:17.950Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:20:19.874Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:20:21.798Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:20:24.608Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:20:24.608Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:20:25.544Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:20:27.619Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:20:27.619Z] 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.
[2024-11-22T23:20:27.619Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:20:27.619Z] Movies recommended for you:
[2024-11-22T23:20:27.619Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:20:27.619Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:20:27.619Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (12810.869 ms) ======
[2024-11-22T23:20:27.619Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-22T23:20:27.619Z] GC before operation: completed in 68.092 ms, heap usage 417.504 MB -> 54.464 MB.
[2024-11-22T23:20:29.736Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:20:30.673Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:20:33.642Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:20:34.579Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:20:42.026Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:20:42.026Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:20:42.026Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:20:42.026Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:20:42.026Z] 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.
[2024-11-22T23:20:42.026Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:20:42.026Z] Movies recommended for you:
[2024-11-22T23:20:42.026Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:20:42.026Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:20:42.026Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12555.088 ms) ======
[2024-11-22T23:20:42.026Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-22T23:20:42.026Z] GC before operation: completed in 68.031 ms, heap usage 79.691 MB -> 50.695 MB.
[2024-11-22T23:20:42.026Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:20:44.127Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:20:46.217Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:20:47.322Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:20:49.252Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:20:50.188Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:20:51.124Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:20:52.059Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:20:52.059Z] 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.
[2024-11-22T23:20:52.059Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:20:53.002Z] Movies recommended for you:
[2024-11-22T23:20:53.002Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:20:53.002Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:20:53.002Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12566.170 ms) ======
[2024-11-22T23:20:53.002Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-22T23:20:53.002Z] GC before operation: completed in 79.266 ms, heap usage 429.039 MB -> 54.429 MB.
[2024-11-22T23:20:54.940Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:20:56.865Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:21:01.615Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:21:01.615Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:21:01.615Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:21:02.721Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:21:03.827Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:21:04.927Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:21:04.927Z] 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.
[2024-11-22T23:21:04.927Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:21:04.927Z] Movies recommended for you:
[2024-11-22T23:21:04.927Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:21:04.927Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:21:04.927Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12687.736 ms) ======
[2024-11-22T23:21:04.927Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-22T23:21:07.285Z] GC before operation: completed in 71.974 ms, heap usage 181.363 MB -> 51.171 MB.
[2024-11-22T23:21:07.285Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:21:09.205Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:21:10.947Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:21:13.049Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:21:13.989Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:21:15.662Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:21:15.662Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:21:16.770Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:21:17.877Z] 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.
[2024-11-22T23:21:17.877Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:21:17.877Z] Movies recommended for you:
[2024-11-22T23:21:17.877Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:21:17.877Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:21:17.877Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12035.575 ms) ======
[2024-11-22T23:21:17.877Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-22T23:21:17.877Z] GC before operation: completed in 66.816 ms, heap usage 417.302 MB -> 54.327 MB.
[2024-11-22T23:21:18.986Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:21:21.096Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:21:23.021Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:21:24.950Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:21:25.889Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:21:27.523Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:21:30.097Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:21:30.097Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:21:30.097Z] 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.
[2024-11-22T23:21:30.097Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:21:30.097Z] Movies recommended for you:
[2024-11-22T23:21:30.097Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:21:30.097Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:21:30.097Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12280.614 ms) ======
[2024-11-22T23:21:30.097Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-22T23:21:30.097Z] GC before operation: completed in 80.376 ms, heap usage 409.013 MB -> 54.426 MB.
[2024-11-22T23:21:31.647Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:21:33.745Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:21:35.667Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:21:36.950Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:21:37.886Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:21:38.838Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:21:39.779Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:21:41.700Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:21:41.700Z] 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.
[2024-11-22T23:21:41.700Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:21:41.700Z] Movies recommended for you:
[2024-11-22T23:21:41.700Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:21:41.700Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:21:41.700Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (11606.905 ms) ======
[2024-11-22T23:21:41.700Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-22T23:21:41.700Z] GC before operation: completed in 76.913 ms, heap usage 81.477 MB -> 51.244 MB.
[2024-11-22T23:21:43.633Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T23:21:45.553Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T23:21:51.593Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T23:21:51.594Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T23:21:51.594Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T23:21:51.594Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T23:21:52.536Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T23:21:53.472Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T23:21:53.472Z] 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.
[2024-11-22T23:21:53.472Z] The best model improves the baseline by 14.52%.
[2024-11-22T23:21:53.472Z] Movies recommended for you:
[2024-11-22T23:21:53.472Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T23:21:53.472Z] There is no way to check that no silent failure occurred.
[2024-11-22T23:21:53.472Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (11927.401 ms) ======
[2024-11-22T23:21:54.420Z] -----------------------------------
[2024-11-22T23:21:54.420Z] renaissance-movie-lens_0_PASSED
[2024-11-22T23:21:54.420Z] -----------------------------------
[2024-11-22T23:21:54.420Z]
[2024-11-22T23:21:54.420Z] TEST TEARDOWN:
[2024-11-22T23:21:54.420Z] Nothing to be done for teardown.
[2024-11-22T23:21:54.420Z] renaissance-movie-lens_0 Finish Time: Fri Nov 22 23:21:53 2024 Epoch Time (ms): 1732317713472