renaissance-movie-lens_0
[2024-10-03T06:46:23.919Z] Running test renaissance-movie-lens_0 ...
[2024-10-03T06:46:23.919Z] ===============================================
[2024-10-03T06:46:23.919Z] renaissance-movie-lens_0 Start Time: Thu Oct 3 01:46:22 2024 Epoch Time (ms): 1727937982378
[2024-10-03T06:46:23.919Z] variation: NoOptions
[2024-10-03T06:46:23.919Z] JVM_OPTIONS:
[2024-10-03T06:46:23.919Z] { \
[2024-10-03T06:46:23.919Z] echo ""; echo "TEST SETUP:"; \
[2024-10-03T06:46:23.919Z] echo "Nothing to be done for setup."; \
[2024-10-03T06:46:23.919Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279373252500/renaissance-movie-lens_0"; \
[2024-10-03T06:46:23.919Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279373252500/renaissance-movie-lens_0"; \
[2024-10-03T06:46:23.919Z] echo ""; echo "TESTING:"; \
[2024-10-03T06:46:23.919Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-17.0.13+10/bin/..//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_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279373252500/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-03T06:46:23.919Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279373252500/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-03T06:46:23.919Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-03T06:46:23.919Z] echo "Nothing to be done for teardown."; \
[2024-10-03T06:46:23.919Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17279373252500/TestTargetResult";
[2024-10-03T06:46:23.919Z]
[2024-10-03T06:46:23.919Z] TEST SETUP:
[2024-10-03T06:46:23.919Z] Nothing to be done for setup.
[2024-10-03T06:46:23.919Z]
[2024-10-03T06:46:23.919Z] TESTING:
[2024-10-03T06:46:25.341Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-03T06:46:27.539Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-10-03T06:46:30.614Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-03T06:46:30.614Z] Training: 60056, validation: 20285, test: 19854
[2024-10-03T06:46:30.614Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-03T06:46:31.308Z] GC before operation: completed in 57.875 ms, heap usage 134.130 MB -> 37.786 MB.
[2024-10-03T06:46:38.901Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:46:41.987Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:46:45.097Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:46:48.196Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:46:50.416Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:46:51.853Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:46:53.302Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:46:54.752Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:46:55.434Z] 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.
[2024-10-03T06:46:55.434Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:46:55.434Z] Movies recommended for you:
[2024-10-03T06:46:55.434Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:46:55.434Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:46:55.434Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24427.036 ms) ======
[2024-10-03T06:46:55.434Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-03T06:46:55.434Z] GC before operation: completed in 120.567 ms, heap usage 521.102 MB -> 54.644 MB.
[2024-10-03T06:46:58.519Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:47:01.610Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:47:03.837Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:47:06.940Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:47:08.378Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:47:09.812Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:47:11.241Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:47:12.655Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:47:12.655Z] 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.
[2024-10-03T06:47:13.338Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:47:13.338Z] Movies recommended for you:
[2024-10-03T06:47:13.338Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:47:13.338Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:47:13.338Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17563.619 ms) ======
[2024-10-03T06:47:13.338Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-03T06:47:13.338Z] GC before operation: completed in 51.393 ms, heap usage 140.949 MB -> 51.475 MB.
[2024-10-03T06:47:16.639Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:47:18.433Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:47:21.510Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:47:23.768Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:47:25.197Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:47:26.638Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:47:28.049Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:47:29.469Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:47:29.469Z] 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.
[2024-10-03T06:47:29.469Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:47:30.150Z] Movies recommended for you:
[2024-10-03T06:47:30.150Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:47:30.150Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:47:30.150Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16650.821 ms) ======
[2024-10-03T06:47:30.150Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-03T06:47:30.150Z] GC before operation: completed in 88.203 ms, heap usage 281.287 MB -> 52.073 MB.
[2024-10-03T06:47:32.377Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:47:34.620Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:47:36.866Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:47:39.074Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:47:40.488Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:47:41.913Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:47:43.431Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:47:44.847Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:47:44.847Z] 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.
[2024-10-03T06:47:44.847Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:47:44.847Z] Movies recommended for you:
[2024-10-03T06:47:44.847Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:47:44.847Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:47:44.847Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15011.002 ms) ======
[2024-10-03T06:47:44.847Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-03T06:47:44.847Z] GC before operation: completed in 56.597 ms, heap usage 305.988 MB -> 54.284 MB.
[2024-10-03T06:47:47.915Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:47:50.114Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:47:52.333Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:47:54.529Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:47:55.947Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:47:57.360Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:47:58.783Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:48:00.221Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:48:00.221Z] 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.
[2024-10-03T06:48:00.221Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:48:00.221Z] Movies recommended for you:
[2024-10-03T06:48:00.221Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:48:00.221Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:48:00.221Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15372.588 ms) ======
[2024-10-03T06:48:00.221Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-03T06:48:00.221Z] GC before operation: completed in 62.904 ms, heap usage 259.325 MB -> 52.595 MB.
[2024-10-03T06:48:03.306Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:48:05.554Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:48:08.324Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:48:09.742Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:48:11.171Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:48:13.382Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:48:14.062Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:48:16.278Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:48:16.278Z] 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.
[2024-10-03T06:48:16.278Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:48:16.278Z] Movies recommended for you:
[2024-10-03T06:48:16.278Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:48:16.278Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:48:16.278Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15769.535 ms) ======
[2024-10-03T06:48:16.278Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-03T06:48:16.278Z] GC before operation: completed in 86.577 ms, heap usage 180.808 MB -> 52.490 MB.
[2024-10-03T06:48:18.516Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:48:20.735Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:48:22.974Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:48:25.187Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:48:26.605Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:48:28.021Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:48:29.449Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:48:30.857Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:48:30.857Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T06:48:30.857Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:48:31.551Z] Movies recommended for you:
[2024-10-03T06:48:31.551Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:48:31.551Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:48:31.551Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15031.027 ms) ======
[2024-10-03T06:48:31.551Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-03T06:48:31.551Z] GC before operation: completed in 79.318 ms, heap usage 483.112 MB -> 56.127 MB.
[2024-10-03T06:48:33.803Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:48:36.021Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:48:38.248Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:48:40.449Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:48:41.865Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:48:43.337Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:48:44.748Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:48:46.171Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:48:46.171Z] 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.
[2024-10-03T06:48:46.171Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:48:46.171Z] Movies recommended for you:
[2024-10-03T06:48:46.171Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:48:46.171Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:48:46.171Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14968.475 ms) ======
[2024-10-03T06:48:46.171Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-03T06:48:46.171Z] GC before operation: completed in 59.091 ms, heap usage 320.898 MB -> 53.059 MB.
[2024-10-03T06:48:49.267Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:48:51.491Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:48:53.708Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:48:55.940Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:48:57.358Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:48:58.230Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:48:59.734Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:49:01.158Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:49:01.158Z] 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.
[2024-10-03T06:49:01.158Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:49:01.158Z] Movies recommended for you:
[2024-10-03T06:49:01.158Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:49:01.158Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:49:01.158Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14970.084 ms) ======
[2024-10-03T06:49:01.159Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-03T06:49:01.159Z] GC before operation: completed in 88.803 ms, heap usage 151.712 MB -> 52.673 MB.
[2024-10-03T06:49:04.249Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:49:06.451Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:49:08.677Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:49:10.880Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:49:12.307Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:49:13.000Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:49:15.215Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:49:15.926Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:49:16.613Z] 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.
[2024-10-03T06:49:16.613Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:49:16.613Z] Movies recommended for you:
[2024-10-03T06:49:16.613Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:49:16.613Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:49:16.613Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15033.506 ms) ======
[2024-10-03T06:49:16.613Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-03T06:49:16.613Z] GC before operation: completed in 57.541 ms, heap usage 164.559 MB -> 52.733 MB.
[2024-10-03T06:49:18.830Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:49:21.036Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:49:23.270Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:49:25.489Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:49:26.905Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:49:28.331Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:49:29.754Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:49:31.177Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:49:31.177Z] 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.
[2024-10-03T06:49:31.177Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:49:31.177Z] Movies recommended for you:
[2024-10-03T06:49:31.177Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:49:31.177Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:49:31.177Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14586.574 ms) ======
[2024-10-03T06:49:31.177Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-03T06:49:31.177Z] GC before operation: completed in 77.253 ms, heap usage 352.796 MB -> 52.704 MB.
[2024-10-03T06:49:33.396Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:49:35.604Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:49:37.823Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:49:40.031Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:49:41.442Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:49:42.868Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:49:44.283Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:49:45.717Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:49:45.717Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T06:49:45.717Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:49:45.717Z] Movies recommended for you:
[2024-10-03T06:49:45.717Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:49:45.717Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:49:45.717Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14573.247 ms) ======
[2024-10-03T06:49:45.717Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-03T06:49:45.717Z] GC before operation: completed in 53.834 ms, heap usage 97.111 MB -> 56.148 MB.
[2024-10-03T06:49:48.814Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:49:51.022Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:49:53.254Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:49:55.486Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:49:56.177Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:49:57.605Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:49:59.038Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:50:01.285Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:50:01.285Z] 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.
[2024-10-03T06:50:01.285Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:50:01.285Z] Movies recommended for you:
[2024-10-03T06:50:01.285Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:50:01.285Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:50:01.285Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15365.666 ms) ======
[2024-10-03T06:50:01.285Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-03T06:50:01.285Z] GC before operation: completed in 58.065 ms, heap usage 127.220 MB -> 53.942 MB.
[2024-10-03T06:50:03.492Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:50:05.710Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:50:08.239Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:50:10.451Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:50:11.864Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:50:13.277Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:50:14.692Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:50:16.119Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:50:16.119Z] 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.
[2024-10-03T06:50:16.119Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:50:16.119Z] Movies recommended for you:
[2024-10-03T06:50:16.119Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:50:16.119Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:50:16.119Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14874.589 ms) ======
[2024-10-03T06:50:16.119Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-03T06:50:16.119Z] GC before operation: completed in 74.387 ms, heap usage 446.222 MB -> 52.808 MB.
[2024-10-03T06:50:19.219Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:50:21.449Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:50:23.693Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:50:25.107Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:50:26.520Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:50:28.050Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:50:29.500Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:50:30.940Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:50:30.940Z] 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.
[2024-10-03T06:50:30.940Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:50:30.940Z] Movies recommended for you:
[2024-10-03T06:50:30.940Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:50:30.940Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:50:30.940Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14750.229 ms) ======
[2024-10-03T06:50:30.940Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-03T06:50:30.940Z] GC before operation: completed in 61.270 ms, heap usage 479.172 MB -> 56.354 MB.
[2024-10-03T06:50:34.020Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:50:36.254Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:50:38.470Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:50:40.688Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:50:41.377Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:50:42.811Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:50:44.273Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:50:45.714Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:50:45.714Z] 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.
[2024-10-03T06:50:45.714Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:50:46.392Z] Movies recommended for you:
[2024-10-03T06:50:46.392Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:50:46.392Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:50:46.392Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15049.782 ms) ======
[2024-10-03T06:50:46.392Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-03T06:50:46.392Z] GC before operation: completed in 82.323 ms, heap usage 75.697 MB -> 56.410 MB.
[2024-10-03T06:50:48.592Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:50:50.802Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:50:53.024Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:50:55.255Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:50:56.686Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:50:58.109Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:50:59.544Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:51:00.242Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:51:00.938Z] 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.
[2024-10-03T06:51:00.938Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:51:00.938Z] Movies recommended for you:
[2024-10-03T06:51:00.938Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:51:00.938Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:51:00.938Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14714.453 ms) ======
[2024-10-03T06:51:00.938Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-03T06:51:00.938Z] GC before operation: completed in 87.538 ms, heap usage 451.836 MB -> 53.015 MB.
[2024-10-03T06:51:03.216Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:51:05.441Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:51:08.212Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:51:09.650Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:51:11.062Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:51:12.495Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:51:13.915Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:51:15.332Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:51:15.332Z] 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.
[2024-10-03T06:51:15.332Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:51:16.029Z] Movies recommended for you:
[2024-10-03T06:51:16.029Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:51:16.029Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:51:16.029Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14747.978 ms) ======
[2024-10-03T06:51:16.029Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-03T06:51:16.029Z] GC before operation: completed in 97.972 ms, heap usage 293.403 MB -> 52.907 MB.
[2024-10-03T06:51:18.252Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:51:20.471Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:51:22.709Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:51:24.919Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:51:26.340Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:51:27.759Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:51:29.172Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:51:30.619Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:51:30.619Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T06:51:30.619Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:51:30.620Z] Movies recommended for you:
[2024-10-03T06:51:30.620Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:51:30.620Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:51:30.620Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14803.781 ms) ======
[2024-10-03T06:51:30.620Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-03T06:51:30.620Z] GC before operation: completed in 56.096 ms, heap usage 100.132 MB -> 54.685 MB.
[2024-10-03T06:51:32.846Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T06:51:35.058Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T06:51:37.279Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T06:51:39.496Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T06:51:40.917Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T06:51:42.347Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T06:51:43.756Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T06:51:45.182Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T06:51:45.182Z] 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.
[2024-10-03T06:51:45.182Z] The best model improves the baseline by 14.43%.
[2024-10-03T06:51:45.182Z] Movies recommended for you:
[2024-10-03T06:51:45.182Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T06:51:45.182Z] There is no way to check that no silent failure occurred.
[2024-10-03T06:51:45.182Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14615.942 ms) ======
[2024-10-03T06:51:46.593Z] -----------------------------------
[2024-10-03T06:51:46.593Z] renaissance-movie-lens_0_PASSED
[2024-10-03T06:51:46.593Z] -----------------------------------
[2024-10-03T06:51:46.593Z]
[2024-10-03T06:51:46.593Z] TEST TEARDOWN:
[2024-10-03T06:51:46.593Z] Nothing to be done for teardown.
[2024-10-03T06:51:46.593Z] renaissance-movie-lens_0 Finish Time: Thu Oct 3 01:51:46 2024 Epoch Time (ms): 1727938306238