renaissance-movie-lens_0
[2024-11-23T00:16:48.729Z] Running test renaissance-movie-lens_0 ...
[2024-11-23T00:16:48.729Z] ===============================================
[2024-11-23T00:16:48.729Z] renaissance-movie-lens_0 Start Time: Sat Nov 23 00:16:48 2024 Epoch Time (ms): 1732321008129
[2024-11-23T00:16:48.729Z] variation: NoOptions
[2024-11-23T00:16:48.729Z] JVM_OPTIONS:
[2024-11-23T00:16:48.729Z] { \
[2024-11-23T00:16:48.729Z] echo ""; echo "TEST SETUP:"; \
[2024-11-23T00:16:48.729Z] echo "Nothing to be done for setup."; \
[2024-11-23T00:16:48.729Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323198919990/renaissance-movie-lens_0"; \
[2024-11-23T00:16:48.729Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323198919990/renaissance-movie-lens_0"; \
[2024-11-23T00:16:48.729Z] echo ""; echo "TESTING:"; \
[2024-11-23T00:16:48.729Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_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_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323198919990/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-23T00:16:48.729Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323198919990/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-23T00:16:48.729Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-23T00:16:48.729Z] echo "Nothing to be done for teardown."; \
[2024-11-23T00:16:48.729Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17323198919990/TestTargetResult";
[2024-11-23T00:16:48.729Z]
[2024-11-23T00:16:48.729Z] TEST SETUP:
[2024-11-23T00:16:48.729Z] Nothing to be done for setup.
[2024-11-23T00:16:48.729Z]
[2024-11-23T00:16:48.729Z] TESTING:
[2024-11-23T00:16:52.752Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-23T00:16:55.679Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-23T00:17:03.675Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-23T00:17:03.675Z] Training: 60056, validation: 20285, test: 19854
[2024-11-23T00:17:03.675Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-23T00:17:03.675Z] GC before operation: completed in 104.657 ms, heap usage 211.584 MB -> 37.204 MB.
[2024-11-23T00:17:15.454Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:17:22.007Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:17:27.246Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:17:31.287Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:17:46.656Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:17:46.656Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:17:46.656Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:17:46.656Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:17:46.656Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:17:46.656Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:17:46.656Z] Movies recommended for you:
[2024-11-23T00:17:46.656Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:17:46.656Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:17:46.656Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (39572.679 ms) ======
[2024-11-23T00:17:46.656Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-23T00:17:46.656Z] GC before operation: completed in 192.596 ms, heap usage 271.795 MB -> 49.594 MB.
[2024-11-23T00:17:47.582Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:17:51.642Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:17:55.682Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:17:59.725Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:18:02.138Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:18:04.562Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:18:07.504Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:18:09.410Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:18:10.332Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:18:10.332Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:18:10.332Z] Movies recommended for you:
[2024-11-23T00:18:10.332Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:18:10.332Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:18:10.332Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (27090.532 ms) ======
[2024-11-23T00:18:10.332Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-23T00:18:10.332Z] GC before operation: completed in 188.026 ms, heap usage 236.097 MB -> 50.963 MB.
[2024-11-23T00:18:14.871Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:18:19.423Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:18:23.455Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:18:25.352Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:18:28.297Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:18:30.197Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:18:33.138Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:18:35.569Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:18:36.493Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:18:36.493Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:18:36.493Z] Movies recommended for you:
[2024-11-23T00:18:36.493Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:18:36.493Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:18:36.493Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (25300.325 ms) ======
[2024-11-23T00:18:36.493Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-23T00:18:36.493Z] GC before operation: completed in 180.378 ms, heap usage 165.398 MB -> 51.307 MB.
[2024-11-23T00:18:39.433Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:18:43.990Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:18:46.927Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:18:58.810Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:18:58.810Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:18:58.810Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:18:58.810Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:19:01.443Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:19:01.443Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:19:01.443Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:19:01.443Z] Movies recommended for you:
[2024-11-23T00:19:01.443Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:19:01.443Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:19:01.443Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (23621.555 ms) ======
[2024-11-23T00:19:01.443Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-23T00:19:01.443Z] GC before operation: completed in 182.415 ms, heap usage 523.291 MB -> 55.137 MB.
[2024-11-23T00:19:03.342Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:19:07.379Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:19:10.376Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:19:13.313Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:19:16.238Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:19:18.142Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:19:20.559Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:19:22.461Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:19:23.386Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:19:23.386Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:19:23.386Z] Movies recommended for you:
[2024-11-23T00:19:23.386Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:19:23.386Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:19:23.386Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (23583.692 ms) ======
[2024-11-23T00:19:23.386Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-23T00:19:23.386Z] GC before operation: completed in 191.320 ms, heap usage 179.534 MB -> 51.839 MB.
[2024-11-23T00:19:27.348Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:19:30.286Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:19:34.325Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:19:37.255Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:19:39.158Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:19:42.096Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:19:44.245Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:19:46.148Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:19:46.148Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:19:47.075Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:19:47.075Z] Movies recommended for you:
[2024-11-23T00:19:47.075Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:19:47.075Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:19:47.075Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (23150.943 ms) ======
[2024-11-23T00:19:47.075Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-23T00:19:47.075Z] GC before operation: completed in 186.553 ms, heap usage 555.929 MB -> 55.269 MB.
[2024-11-23T00:19:50.016Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:19:54.059Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:19:56.989Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:20:01.031Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:20:02.936Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:20:04.834Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:20:16.471Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:20:16.471Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:20:16.471Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:20:16.471Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:20:16.471Z] Movies recommended for you:
[2024-11-23T00:20:16.471Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:20:16.471Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:20:16.471Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (23247.284 ms) ======
[2024-11-23T00:20:16.471Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-23T00:20:16.471Z] GC before operation: completed in 190.157 ms, heap usage 508.650 MB -> 55.392 MB.
[2024-11-23T00:20:16.471Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:20:17.393Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:20:21.435Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:20:24.374Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:20:26.268Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:20:28.170Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:20:31.111Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:20:32.548Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:20:33.531Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:20:33.531Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:20:33.531Z] Movies recommended for you:
[2024-11-23T00:20:33.531Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:20:33.531Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:20:33.531Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (23213.320 ms) ======
[2024-11-23T00:20:33.531Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-23T00:20:33.531Z] GC before operation: completed in 184.709 ms, heap usage 213.652 MB -> 52.249 MB.
[2024-11-23T00:20:37.587Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:20:40.522Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:20:44.731Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:20:47.671Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:20:49.563Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:20:51.463Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:20:54.405Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:20:56.340Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:20:56.340Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:20:56.340Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:20:57.268Z] Movies recommended for you:
[2024-11-23T00:20:57.268Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:20:57.268Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:20:57.268Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (23198.452 ms) ======
[2024-11-23T00:20:57.268Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-23T00:20:57.268Z] GC before operation: completed in 190.400 ms, heap usage 168.315 MB -> 52.018 MB.
[2024-11-23T00:21:01.310Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:21:03.753Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:21:07.798Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:21:10.732Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:21:14.199Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:21:16.099Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:21:18.000Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:21:19.898Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:21:32.374Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:21:32.374Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:21:32.374Z] Movies recommended for you:
[2024-11-23T00:21:32.374Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:21:32.374Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:21:32.374Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (23708.031 ms) ======
[2024-11-23T00:21:32.374Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-23T00:21:32.374Z] GC before operation: completed in 218.926 ms, heap usage 506.624 MB -> 55.616 MB.
[2024-11-23T00:21:32.374Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:21:32.374Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:21:35.398Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:21:35.398Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:21:36.325Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:21:40.142Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:21:41.063Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:21:42.956Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:21:44.058Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:21:44.058Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:21:44.058Z] Movies recommended for you:
[2024-11-23T00:21:44.058Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:21:44.058Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:21:44.058Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (22515.635 ms) ======
[2024-11-23T00:21:44.058Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-23T00:21:44.058Z] GC before operation: completed in 206.970 ms, heap usage 192.021 MB -> 51.862 MB.
[2024-11-23T00:21:47.519Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:21:50.452Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:21:53.383Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:21:56.460Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:21:59.395Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:22:01.295Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:22:03.193Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:22:05.091Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:22:06.019Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:22:06.019Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:22:06.019Z] Movies recommended for you:
[2024-11-23T00:22:06.019Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:22:06.019Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:22:06.019Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (22343.428 ms) ======
[2024-11-23T00:22:06.019Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-23T00:22:06.019Z] GC before operation: completed in 189.283 ms, heap usage 84.384 MB -> 54.997 MB.
[2024-11-23T00:22:10.049Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:22:12.990Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:22:17.045Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:22:19.999Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:22:22.413Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:22:24.306Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:22:27.250Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:22:29.154Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:22:29.154Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:22:29.154Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:22:30.090Z] Movies recommended for you:
[2024-11-23T00:22:30.090Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:22:30.090Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:22:30.090Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (23385.697 ms) ======
[2024-11-23T00:22:30.090Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-23T00:22:30.090Z] GC before operation: completed in 201.677 ms, heap usage 475.337 MB -> 52.425 MB.
[2024-11-23T00:22:33.026Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:22:37.603Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:22:47.148Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:22:47.148Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:22:47.148Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:22:48.578Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:22:50.488Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:22:52.395Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:22:52.395Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:22:53.318Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:22:53.318Z] Movies recommended for you:
[2024-11-23T00:22:53.318Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:22:53.318Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:22:53.318Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (23057.154 ms) ======
[2024-11-23T00:22:53.318Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-23T00:22:53.318Z] GC before operation: completed in 198.846 ms, heap usage 570.808 MB -> 55.502 MB.
[2024-11-23T00:22:56.249Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:23:00.297Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:23:03.235Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:23:07.276Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:23:09.181Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:23:10.618Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:23:13.570Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:23:15.482Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:23:16.409Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:23:16.409Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:23:16.409Z] Movies recommended for you:
[2024-11-23T00:23:16.409Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:23:16.409Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:23:16.409Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (23014.586 ms) ======
[2024-11-23T00:23:16.409Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-23T00:23:16.409Z] GC before operation: completed in 204.399 ms, heap usage 253.984 MB -> 52.207 MB.
[2024-11-23T00:23:20.029Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:23:23.129Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:23:26.064Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:23:30.654Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:23:31.579Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:23:34.520Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:23:36.420Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:23:38.326Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:23:39.611Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:23:39.611Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:23:39.611Z] Movies recommended for you:
[2024-11-23T00:23:39.611Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:23:39.611Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:23:39.611Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (22639.710 ms) ======
[2024-11-23T00:23:39.611Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-23T00:23:39.611Z] GC before operation: completed in 214.942 ms, heap usage 480.684 MB -> 55.691 MB.
[2024-11-23T00:23:43.479Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:23:46.421Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:23:49.360Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:23:56.158Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:23:56.158Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:23:57.418Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:23:58.345Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:24:01.273Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:24:01.273Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:24:01.273Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:24:01.273Z] Movies recommended for you:
[2024-11-23T00:24:01.273Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:24:01.273Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:24:01.273Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (22313.447 ms) ======
[2024-11-23T00:24:01.273Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-23T00:24:02.196Z] GC before operation: completed in 197.971 ms, heap usage 107.983 MB -> 51.954 MB.
[2024-11-23T00:24:05.129Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:24:09.183Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:24:12.117Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:24:15.049Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:24:17.494Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:24:19.386Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:24:21.287Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:24:23.189Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:24:24.117Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:24:24.117Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:24:24.117Z] Movies recommended for you:
[2024-11-23T00:24:24.117Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:24:24.117Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:24:24.117Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (22456.903 ms) ======
[2024-11-23T00:24:24.117Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-23T00:24:24.117Z] GC before operation: completed in 198.180 ms, heap usage 176.430 MB -> 52.132 MB.
[2024-11-23T00:24:28.154Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:24:31.604Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:24:34.544Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:24:38.593Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:24:40.496Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:24:42.396Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:24:45.331Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:24:47.235Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:24:47.235Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:24:47.235Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:24:48.162Z] Movies recommended for you:
[2024-11-23T00:24:48.162Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:24:48.162Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:24:48.162Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (23268.579 ms) ======
[2024-11-23T00:24:48.162Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-23T00:24:48.162Z] GC before operation: completed in 213.686 ms, heap usage 147.410 MB -> 52.304 MB.
[2024-11-23T00:24:51.498Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T00:24:54.446Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T00:25:05.522Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T00:25:07.616Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T00:25:07.616Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T00:25:07.616Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T00:25:07.616Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T00:25:09.519Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T00:25:10.449Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-23T00:25:10.449Z] The best model improves the baseline by 14.43%.
[2024-11-23T00:25:10.449Z] Movies recommended for you:
[2024-11-23T00:25:10.449Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T00:25:10.449Z] There is no way to check that no silent failure occurred.
[2024-11-23T00:25:10.449Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (22419.928 ms) ======
[2024-11-23T00:25:12.378Z] -----------------------------------
[2024-11-23T00:25:12.378Z] renaissance-movie-lens_0_PASSED
[2024-11-23T00:25:12.378Z] -----------------------------------
[2024-11-23T00:25:12.378Z]
[2024-11-23T00:25:12.378Z] TEST TEARDOWN:
[2024-11-23T00:25:12.378Z] Nothing to be done for teardown.
[2024-11-23T00:25:12.378Z] renaissance-movie-lens_0 Finish Time: Sat Nov 23 00:25:11 2024 Epoch Time (ms): 1732321511771