renaissance-movie-lens_0
[2024-06-26T20:17:31.683Z] Running test renaissance-movie-lens_0 ...
[2024-06-26T20:17:31.683Z] ===============================================
[2024-06-26T20:17:31.683Z] renaissance-movie-lens_0 Start Time: Wed Jun 26 20:17:31 2024 Epoch Time (ms): 1719433051455
[2024-06-26T20:17:31.683Z] variation: NoOptions
[2024-06-26T20:17:31.683Z] JVM_OPTIONS:
[2024-06-26T20:17:31.683Z] { \
[2024-06-26T20:17:31.683Z] echo ""; echo "TEST SETUP:"; \
[2024-06-26T20:17:31.683Z] echo "Nothing to be done for setup."; \
[2024-06-26T20:17:31.683Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17194321026618/renaissance-movie-lens_0"; \
[2024-06-26T20:17:31.683Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17194321026618/renaissance-movie-lens_0"; \
[2024-06-26T20:17:31.683Z] echo ""; echo "TESTING:"; \
[2024-06-26T20:17:31.683Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17194321026618/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-26T20:17:31.683Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17194321026618/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-26T20:17:31.683Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-26T20:17:31.683Z] echo "Nothing to be done for teardown."; \
[2024-06-26T20:17:31.683Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17194321026618/TestTargetResult";
[2024-06-26T20:17:31.683Z]
[2024-06-26T20:17:31.683Z] TEST SETUP:
[2024-06-26T20:17:31.683Z] Nothing to be done for setup.
[2024-06-26T20:17:31.683Z]
[2024-06-26T20:17:31.683Z] TESTING:
[2024-06-26T20:17:35.778Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-26T20:17:38.756Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-06-26T20:17:44.108Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-26T20:17:44.108Z] Training: 60056, validation: 20285, test: 19854
[2024-06-26T20:17:44.108Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-26T20:17:44.108Z] GC before operation: completed in 72.182 ms, heap usage 58.828 MB -> 39.554 MB.
[2024-06-26T20:17:53.968Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:17:58.072Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:18:02.182Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:18:05.879Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:18:07.831Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:18:09.762Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:18:12.753Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:18:14.688Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:18:14.688Z] 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.
[2024-06-26T20:18:15.630Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:18:15.630Z] Movies recommended for you:
[2024-06-26T20:18:15.630Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:18:15.630Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:18:15.630Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (31038.414 ms) ======
[2024-06-26T20:18:15.630Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-26T20:18:15.630Z] GC before operation: completed in 128.473 ms, heap usage 825.354 MB -> 56.480 MB.
[2024-06-26T20:18:19.741Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:18:22.732Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:18:25.719Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:18:29.888Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:18:31.821Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:18:33.757Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:18:35.686Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:18:37.629Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:18:37.629Z] 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.
[2024-06-26T20:18:37.629Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:18:37.629Z] Movies recommended for you:
[2024-06-26T20:18:37.629Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:18:37.629Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:18:37.629Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (22588.644 ms) ======
[2024-06-26T20:18:37.629Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-26T20:18:38.569Z] GC before operation: completed in 110.248 ms, heap usage 653.926 MB -> 56.806 MB.
[2024-06-26T20:18:41.561Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:18:44.558Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:18:47.542Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:18:50.522Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:18:52.451Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:18:54.379Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:18:56.314Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:18:58.251Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:18:58.251Z] 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.
[2024-06-26T20:18:58.251Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:18:58.251Z] Movies recommended for you:
[2024-06-26T20:18:58.251Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:18:58.251Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:18:58.251Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20340.808 ms) ======
[2024-06-26T20:18:58.251Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-26T20:18:58.251Z] GC before operation: completed in 111.328 ms, heap usage 729.928 MB -> 57.304 MB.
[2024-06-26T20:19:02.358Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:19:05.341Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:19:07.269Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:19:10.948Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:19:11.890Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:19:13.826Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:19:15.753Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:19:17.682Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:19:17.682Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-26T20:19:17.682Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:19:17.682Z] Movies recommended for you:
[2024-06-26T20:19:17.682Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:19:17.682Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:19:17.682Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (19095.313 ms) ======
[2024-06-26T20:19:17.682Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-26T20:19:17.682Z] GC before operation: completed in 120.080 ms, heap usage 458.745 MB -> 54.183 MB.
[2024-06-26T20:19:20.662Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:19:23.643Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:19:26.622Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:19:30.728Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:19:32.658Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:19:34.592Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:19:36.527Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:19:38.454Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:19:38.454Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-26T20:19:38.454Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:19:39.393Z] Movies recommended for you:
[2024-06-26T20:19:39.393Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:19:39.393Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:19:39.393Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21166.648 ms) ======
[2024-06-26T20:19:39.393Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-26T20:19:39.393Z] GC before operation: completed in 105.640 ms, heap usage 126.277 MB -> 54.286 MB.
[2024-06-26T20:19:42.370Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:19:46.472Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:19:49.456Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:19:53.562Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:19:55.494Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:19:57.428Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:19:59.362Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:20:01.307Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:20:01.307Z] 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.
[2024-06-26T20:20:01.307Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:20:02.248Z] Movies recommended for you:
[2024-06-26T20:20:02.248Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:20:02.248Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:20:02.248Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (22807.909 ms) ======
[2024-06-26T20:20:02.248Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-26T20:20:02.248Z] GC before operation: completed in 128.071 ms, heap usage 207.930 MB -> 56.429 MB.
[2024-06-26T20:20:06.358Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:20:09.419Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:20:12.930Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:20:14.858Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:20:16.787Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:20:18.712Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:20:20.637Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:20:21.574Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:20:22.513Z] 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.
[2024-06-26T20:20:22.513Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:20:22.513Z] Movies recommended for you:
[2024-06-26T20:20:22.513Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:20:22.513Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:20:22.513Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20300.711 ms) ======
[2024-06-26T20:20:22.513Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-26T20:20:22.513Z] GC before operation: completed in 117.671 ms, heap usage 1.269 GB -> 61.456 MB.
[2024-06-26T20:20:25.485Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:20:28.458Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:20:31.430Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:20:33.354Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:20:35.279Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:20:37.233Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:20:39.159Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:20:41.087Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:20:41.087Z] 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.
[2024-06-26T20:20:41.087Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:20:41.087Z] Movies recommended for you:
[2024-06-26T20:20:41.087Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:20:41.087Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:20:41.087Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (18698.372 ms) ======
[2024-06-26T20:20:41.087Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-26T20:20:41.087Z] GC before operation: completed in 118.042 ms, heap usage 834.689 MB -> 62.900 MB.
[2024-06-26T20:20:44.066Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:20:47.049Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:20:50.026Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:20:53.094Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:20:55.022Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:20:56.947Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:20:58.885Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:20:59.827Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:21:00.767Z] 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.
[2024-06-26T20:21:00.767Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:21:00.767Z] Movies recommended for you:
[2024-06-26T20:21:00.767Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:21:00.767Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:21:00.767Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19327.647 ms) ======
[2024-06-26T20:21:00.767Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-26T20:21:00.767Z] GC before operation: completed in 108.568 ms, heap usage 414.394 MB -> 54.722 MB.
[2024-06-26T20:21:03.738Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:21:05.662Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:21:08.634Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:21:11.607Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:21:12.545Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:21:14.469Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:21:16.271Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:21:18.202Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:21:18.202Z] 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.
[2024-06-26T20:21:18.202Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:21:18.202Z] Movies recommended for you:
[2024-06-26T20:21:18.202Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:21:18.202Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:21:18.202Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17762.026 ms) ======
[2024-06-26T20:21:18.202Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-26T20:21:18.202Z] GC before operation: completed in 107.598 ms, heap usage 426.919 MB -> 54.820 MB.
[2024-06-26T20:21:22.299Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:21:24.224Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:21:27.201Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:21:30.184Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:21:31.121Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:21:33.054Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:21:34.981Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:21:35.921Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:21:36.862Z] 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.
[2024-06-26T20:21:36.862Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:21:36.862Z] Movies recommended for you:
[2024-06-26T20:21:36.862Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:21:36.862Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:21:36.862Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (18295.422 ms) ======
[2024-06-26T20:21:36.862Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-26T20:21:36.862Z] GC before operation: completed in 119.684 ms, heap usage 88.927 MB -> 59.995 MB.
[2024-06-26T20:21:39.843Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:21:42.818Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:21:45.809Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:21:47.918Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:21:49.846Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:21:51.776Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:21:53.785Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:21:54.723Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:21:55.661Z] 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.
[2024-06-26T20:21:55.661Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:21:55.661Z] Movies recommended for you:
[2024-06-26T20:21:55.661Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:21:55.661Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:21:55.661Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (18460.446 ms) ======
[2024-06-26T20:21:55.661Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-26T20:21:55.661Z] GC before operation: completed in 124.993 ms, heap usage 489.582 MB -> 55.846 MB.
[2024-06-26T20:21:58.637Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:00.566Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:03.566Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:06.549Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:08.483Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:09.425Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:11.355Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:13.289Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:13.289Z] 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.
[2024-06-26T20:22:13.289Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:22:14.230Z] Movies recommended for you:
[2024-06-26T20:22:14.230Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:14.230Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:14.230Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (18143.853 ms) ======
[2024-06-26T20:22:14.230Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-26T20:22:14.230Z] GC before operation: completed in 114.972 ms, heap usage 337.988 MB -> 55.015 MB.
[2024-06-26T20:22:17.211Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:20.946Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:23.926Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:26.910Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:28.839Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:30.768Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:32.698Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:34.643Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:34.643Z] 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.
[2024-06-26T20:22:34.643Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:22:34.643Z] Movies recommended for you:
[2024-06-26T20:22:34.643Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:34.643Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:34.643Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (21001.407 ms) ======
[2024-06-26T20:22:34.643Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-26T20:22:34.643Z] GC before operation: completed in 110.081 ms, heap usage 535.773 MB -> 57.939 MB.
[2024-06-26T20:22:37.622Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:40.602Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:43.579Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:46.563Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:48.496Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:50.427Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:52.365Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:54.297Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:55.239Z] 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.
[2024-06-26T20:22:55.239Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:22:55.239Z] Movies recommended for you:
[2024-06-26T20:22:55.239Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:55.239Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:55.239Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19994.866 ms) ======
[2024-06-26T20:22:55.239Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-26T20:22:55.239Z] GC before operation: completed in 114.013 ms, heap usage 225.218 MB -> 54.500 MB.
[2024-06-26T20:22:58.221Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:02.327Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:04.258Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:07.247Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:08.192Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:10.135Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:12.070Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:13.012Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:13.954Z] 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.
[2024-06-26T20:23:13.954Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:23:13.954Z] Movies recommended for you:
[2024-06-26T20:23:13.954Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:13.954Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:13.954Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (18846.435 ms) ======
[2024-06-26T20:23:13.954Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-26T20:23:13.954Z] GC before operation: completed in 129.021 ms, heap usage 222.152 MB -> 54.580 MB.
[2024-06-26T20:23:16.944Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:19.934Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:22.988Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:25.623Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:26.564Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:28.495Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:30.427Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:32.359Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:32.359Z] 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.
[2024-06-26T20:23:32.359Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:23:32.359Z] Movies recommended for you:
[2024-06-26T20:23:32.359Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:32.359Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:32.359Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (18365.522 ms) ======
[2024-06-26T20:23:32.359Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-26T20:23:32.359Z] GC before operation: completed in 121.124 ms, heap usage 262.263 MB -> 54.599 MB.
[2024-06-26T20:23:35.351Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:38.335Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:41.315Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:43.246Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:45.176Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:47.111Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:49.044Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:49.986Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:50.925Z] 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.
[2024-06-26T20:23:50.925Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:23:50.925Z] Movies recommended for you:
[2024-06-26T20:23:50.925Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:50.925Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:50.925Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (18050.357 ms) ======
[2024-06-26T20:23:50.925Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-26T20:23:50.925Z] GC before operation: completed in 125.174 ms, heap usage 932.394 MB -> 58.444 MB.
[2024-06-26T20:23:54.002Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:56.989Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:59.970Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:24:01.902Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:24:03.832Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:24:04.772Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:24:06.700Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:24:08.635Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:24:08.635Z] 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.
[2024-06-26T20:24:08.635Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:24:08.635Z] Movies recommended for you:
[2024-06-26T20:24:08.635Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:24:08.635Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:24:08.635Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (18162.301 ms) ======
[2024-06-26T20:24:08.635Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-26T20:24:08.635Z] GC before operation: completed in 126.775 ms, heap usage 317.476 MB -> 59.486 MB.
[2024-06-26T20:24:11.624Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:24:14.611Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:24:18.804Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:24:20.737Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:24:22.671Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:24:24.603Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:24:26.536Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:24:27.486Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:24:29.116Z] 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.
[2024-06-26T20:24:29.116Z] The best model improves the baseline by 14.43%.
[2024-06-26T20:24:29.116Z] Movies recommended for you:
[2024-06-26T20:24:29.116Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:24:29.116Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:24:29.116Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19346.330 ms) ======
[2024-06-26T20:24:30.057Z] -----------------------------------
[2024-06-26T20:24:30.057Z] renaissance-movie-lens_0_PASSED
[2024-06-26T20:24:30.057Z] -----------------------------------
[2024-06-26T20:24:30.057Z]
[2024-06-26T20:24:30.057Z] TEST TEARDOWN:
[2024-06-26T20:24:30.057Z] Nothing to be done for teardown.
[2024-06-26T20:24:30.057Z] renaissance-movie-lens_0 Finish Time: Wed Jun 26 20:24:29 2024 Epoch Time (ms): 1719433469946