renaissance-movie-lens_0
[2024-10-15T23:38:57.676Z] Running test renaissance-movie-lens_0 ...
[2024-10-15T23:38:57.676Z] ===============================================
[2024-10-15T23:38:57.676Z] renaissance-movie-lens_0 Start Time: Tue Oct 15 23:38:57 2024 Epoch Time (ms): 1729035537441
[2024-10-15T23:38:57.676Z] variation: NoOptions
[2024-10-15T23:38:57.676Z] JVM_OPTIONS:
[2024-10-15T23:38:57.676Z] { \
[2024-10-15T23:38:57.676Z] echo ""; echo "TEST SETUP:"; \
[2024-10-15T23:38:57.676Z] echo "Nothing to be done for setup."; \
[2024-10-15T23:38:57.676Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17290342637806/renaissance-movie-lens_0"; \
[2024-10-15T23:38:57.676Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17290342637806/renaissance-movie-lens_0"; \
[2024-10-15T23:38:57.676Z] echo ""; echo "TESTING:"; \
[2024-10-15T23:38:57.676Z] "/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_17290342637806/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-15T23:38:57.676Z] 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_17290342637806/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-15T23:38:57.676Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-15T23:38:57.676Z] echo "Nothing to be done for teardown."; \
[2024-10-15T23:38:57.676Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17290342637806/TestTargetResult";
[2024-10-15T23:38:57.676Z]
[2024-10-15T23:38:57.676Z] TEST SETUP:
[2024-10-15T23:38:57.676Z] Nothing to be done for setup.
[2024-10-15T23:38:57.676Z]
[2024-10-15T23:38:57.676Z] TESTING:
[2024-10-15T23:39:04.251Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-15T23:39:08.324Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-10-15T23:39:16.489Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-15T23:39:17.421Z] Training: 60056, validation: 20285, test: 19854
[2024-10-15T23:39:17.421Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-15T23:39:17.421Z] GC before operation: completed in 119.417 ms, heap usage 218.912 MB -> 37.236 MB.
[2024-10-15T23:39:30.761Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:39:36.054Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:39:42.678Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:39:46.827Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:39:50.176Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:39:52.090Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:39:55.046Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:39:58.003Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:39:58.003Z] 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.
[2024-10-15T23:39:58.003Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:39:58.937Z] Movies recommended for you:
[2024-10-15T23:39:58.937Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:39:58.937Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:39:58.937Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (41541.454 ms) ======
[2024-10-15T23:39:58.937Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-15T23:39:58.937Z] GC before operation: completed in 206.699 ms, heap usage 296.830 MB -> 49.545 MB.
[2024-10-15T23:40:03.011Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:40:08.285Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:40:11.249Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:40:15.380Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:40:18.338Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:40:20.257Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:40:22.170Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:40:25.125Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:40:25.125Z] 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.
[2024-10-15T23:40:25.125Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:40:25.125Z] Movies recommended for you:
[2024-10-15T23:40:25.125Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:40:25.125Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:40:25.125Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (26685.201 ms) ======
[2024-10-15T23:40:25.125Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-15T23:40:26.074Z] GC before operation: completed in 183.058 ms, heap usage 307.822 MB -> 50.982 MB.
[2024-10-15T23:40:30.153Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:40:33.174Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:40:37.368Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:40:41.442Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:40:43.356Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:40:46.322Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:40:48.239Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:40:51.201Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:40:51.201Z] 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.
[2024-10-15T23:40:51.201Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:40:51.201Z] Movies recommended for you:
[2024-10-15T23:40:51.201Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:40:51.201Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:40:51.201Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (25557.521 ms) ======
[2024-10-15T23:40:51.201Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-15T23:40:51.201Z] GC before operation: completed in 222.740 ms, heap usage 123.060 MB -> 51.252 MB.
[2024-10-15T23:40:55.282Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:40:58.947Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:41:03.025Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:41:05.984Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:41:08.940Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:41:10.853Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:41:13.834Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:41:15.752Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:41:16.698Z] 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.
[2024-10-15T23:41:16.698Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:41:16.698Z] Movies recommended for you:
[2024-10-15T23:41:16.698Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:41:16.698Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:41:16.698Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (25026.797 ms) ======
[2024-10-15T23:41:16.698Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-15T23:41:16.698Z] GC before operation: completed in 220.064 ms, heap usage 275.485 MB -> 51.664 MB.
[2024-10-15T23:41:20.778Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:41:23.740Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:41:27.840Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:41:31.915Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:41:33.831Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:41:35.751Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:41:37.665Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:41:40.623Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:41:40.623Z] 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.
[2024-10-15T23:41:40.623Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:41:40.623Z] Movies recommended for you:
[2024-10-15T23:41:40.623Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:41:40.623Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:41:40.623Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (24171.599 ms) ======
[2024-10-15T23:41:40.623Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-15T23:41:41.557Z] GC before operation: completed in 244.498 ms, heap usage 240.042 MB -> 51.853 MB.
[2024-10-15T23:41:44.518Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:41:48.595Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:41:52.669Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:41:55.630Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:41:58.600Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:42:01.235Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:42:04.211Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:42:06.127Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:42:07.060Z] 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.
[2024-10-15T23:42:07.060Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:42:07.060Z] Movies recommended for you:
[2024-10-15T23:42:07.060Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:42:07.060Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:42:07.060Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (25854.573 ms) ======
[2024-10-15T23:42:07.060Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-15T23:42:07.060Z] GC before operation: completed in 231.221 ms, heap usage 270.606 MB -> 51.761 MB.
[2024-10-15T23:42:11.159Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:42:15.234Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:42:18.191Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:42:22.261Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:42:24.184Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:42:26.103Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:42:29.064Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:42:30.978Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:42:30.978Z] 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.
[2024-10-15T23:42:31.910Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:42:31.910Z] Movies recommended for you:
[2024-10-15T23:42:31.910Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:42:31.910Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:42:31.910Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (24398.286 ms) ======
[2024-10-15T23:42:31.910Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-15T23:42:31.910Z] GC before operation: completed in 181.148 ms, heap usage 155.877 MB -> 51.863 MB.
[2024-10-15T23:42:35.981Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:42:39.033Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:42:43.109Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:42:46.069Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:42:47.987Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:42:50.077Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:42:53.046Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:42:54.964Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:42:55.902Z] 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.
[2024-10-15T23:42:55.902Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:42:55.902Z] Movies recommended for you:
[2024-10-15T23:42:55.902Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:42:55.902Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:42:55.902Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (23987.906 ms) ======
[2024-10-15T23:42:55.902Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-15T23:42:55.902Z] GC before operation: completed in 242.175 ms, heap usage 320.053 MB -> 52.265 MB.
[2024-10-15T23:43:00.665Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:43:03.632Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:43:06.594Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:43:10.664Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:43:12.583Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:43:14.642Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:43:16.571Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:43:18.487Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:43:19.420Z] 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.
[2024-10-15T23:43:19.420Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:43:19.420Z] Movies recommended for you:
[2024-10-15T23:43:19.420Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:43:19.420Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:43:19.420Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (23279.780 ms) ======
[2024-10-15T23:43:19.420Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-15T23:43:19.420Z] GC before operation: completed in 169.687 ms, heap usage 307.582 MB -> 52.094 MB.
[2024-10-15T23:43:23.494Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:43:26.453Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:43:30.528Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:43:33.494Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:43:36.461Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:43:38.495Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:43:40.412Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:43:43.381Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:43:43.381Z] 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.
[2024-10-15T23:43:43.381Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:43:43.381Z] Movies recommended for you:
[2024-10-15T23:43:43.381Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:43:43.381Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:43:43.382Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (24158.671 ms) ======
[2024-10-15T23:43:43.382Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-15T23:43:44.316Z] GC before operation: completed in 207.192 ms, heap usage 242.778 MB -> 52.174 MB.
[2024-10-15T23:43:47.282Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:43:51.361Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:43:55.447Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:43:59.101Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:44:01.019Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:44:02.938Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:44:05.900Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:44:07.817Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:44:07.817Z] 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.
[2024-10-15T23:44:07.817Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:44:08.751Z] Movies recommended for you:
[2024-10-15T23:44:08.751Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:44:08.751Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:44:08.751Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (24545.988 ms) ======
[2024-10-15T23:44:08.751Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-15T23:44:08.751Z] GC before operation: completed in 186.458 ms, heap usage 443.810 MB -> 51.985 MB.
[2024-10-15T23:44:11.732Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:44:15.811Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:44:19.890Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:44:22.882Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:44:24.799Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:44:26.714Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:44:28.630Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:44:31.583Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:44:31.583Z] 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.
[2024-10-15T23:44:31.583Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:44:31.583Z] Movies recommended for you:
[2024-10-15T23:44:31.583Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:44:31.583Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:44:31.583Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (23245.712 ms) ======
[2024-10-15T23:44:31.583Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-15T23:44:32.518Z] GC before operation: completed in 190.288 ms, heap usage 199.346 MB -> 52.079 MB.
[2024-10-15T23:44:36.590Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:44:39.581Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:44:43.652Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:44:47.725Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:44:49.638Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:44:52.662Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:44:54.577Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:44:56.491Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:44:57.427Z] 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.
[2024-10-15T23:44:57.428Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:44:57.428Z] Movies recommended for you:
[2024-10-15T23:44:57.428Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:44:57.428Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:44:57.428Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (25463.248 ms) ======
[2024-10-15T23:44:57.428Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-15T23:44:57.428Z] GC before operation: completed in 203.176 ms, heap usage 235.771 MB -> 55.432 MB.
[2024-10-15T23:45:02.272Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:45:05.236Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:45:09.308Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:45:13.381Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:45:15.294Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:45:17.206Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:45:19.124Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:45:22.095Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:45:22.095Z] 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.
[2024-10-15T23:45:22.095Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:45:22.095Z] Movies recommended for you:
[2024-10-15T23:45:22.095Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:45:22.095Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:45:22.095Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (24597.503 ms) ======
[2024-10-15T23:45:22.095Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-15T23:45:23.026Z] GC before operation: completed in 265.302 ms, heap usage 568.156 MB -> 55.381 MB.
[2024-10-15T23:45:25.991Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:45:30.056Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:45:33.014Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:45:37.177Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:45:39.096Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:45:41.015Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:45:43.977Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:45:45.894Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:45:45.894Z] 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.
[2024-10-15T23:45:45.894Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:45:46.826Z] Movies recommended for you:
[2024-10-15T23:45:46.826Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:45:46.826Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:45:46.826Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (23828.387 ms) ======
[2024-10-15T23:45:46.826Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-15T23:45:46.826Z] GC before operation: completed in 287.166 ms, heap usage 554.883 MB -> 55.539 MB.
[2024-10-15T23:45:50.919Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:45:53.876Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:45:57.950Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:46:01.293Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:46:03.210Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:46:06.168Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:46:08.085Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:46:11.048Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:46:11.048Z] 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.
[2024-10-15T23:46:11.048Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:46:11.048Z] Movies recommended for you:
[2024-10-15T23:46:11.048Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:46:11.048Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:46:11.048Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (24629.460 ms) ======
[2024-10-15T23:46:11.048Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-15T23:46:11.982Z] GC before operation: completed in 272.063 ms, heap usage 281.513 MB -> 52.249 MB.
[2024-10-15T23:46:16.062Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:46:19.021Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:46:23.118Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:46:26.087Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:46:29.050Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:46:30.972Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:46:33.941Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:46:35.857Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:46:36.794Z] 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.
[2024-10-15T23:46:36.794Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:46:36.794Z] Movies recommended for you:
[2024-10-15T23:46:36.794Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:46:36.794Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:46:36.794Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (24999.461 ms) ======
[2024-10-15T23:46:36.794Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-15T23:46:36.794Z] GC before operation: completed in 279.585 ms, heap usage 639.035 MB -> 55.580 MB.
[2024-10-15T23:46:40.884Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:46:44.961Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:46:49.041Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:46:53.162Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:46:55.080Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:46:58.038Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:47:00.644Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:47:03.625Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:47:03.625Z] 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.
[2024-10-15T23:47:03.625Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:47:04.558Z] Movies recommended for you:
[2024-10-15T23:47:04.558Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:47:04.558Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:47:04.558Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (27194.549 ms) ======
[2024-10-15T23:47:04.559Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-15T23:47:04.559Z] GC before operation: completed in 278.009 ms, heap usage 602.826 MB -> 55.597 MB.
[2024-10-15T23:47:08.633Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:47:12.720Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:47:16.804Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:47:20.881Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:47:23.846Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:47:25.762Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:47:28.722Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:47:30.762Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:47:31.698Z] 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.
[2024-10-15T23:47:31.698Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:47:31.698Z] Movies recommended for you:
[2024-10-15T23:47:31.698Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:47:31.698Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:47:31.698Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (27662.167 ms) ======
[2024-10-15T23:47:31.698Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-15T23:47:32.633Z] GC before operation: completed in 270.989 ms, heap usage 597.749 MB -> 55.791 MB.
[2024-10-15T23:47:36.705Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-15T23:47:39.664Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-15T23:47:43.735Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-15T23:47:47.813Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-15T23:47:49.729Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-15T23:47:52.859Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-15T23:47:54.798Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-15T23:47:57.757Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-15T23:47:57.757Z] 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.
[2024-10-15T23:47:57.757Z] The best model improves the baseline by 14.43%.
[2024-10-15T23:47:58.692Z] Movies recommended for you:
[2024-10-15T23:47:58.692Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-15T23:47:58.692Z] There is no way to check that no silent failure occurred.
[2024-10-15T23:47:58.692Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (25965.626 ms) ======
[2024-10-15T23:48:01.293Z] -----------------------------------
[2024-10-15T23:48:01.293Z] renaissance-movie-lens_0_PASSED
[2024-10-15T23:48:01.293Z] -----------------------------------
[2024-10-15T23:48:01.293Z]
[2024-10-15T23:48:01.293Z] TEST TEARDOWN:
[2024-10-15T23:48:01.293Z] Nothing to be done for teardown.
[2024-10-15T23:48:01.293Z] renaissance-movie-lens_0 Finish Time: Tue Oct 15 23:48:00 2024 Epoch Time (ms): 1729036080085