[2024-09-05T20:53:41.762Z] Running test renaissance-movie-lens_0 ... [2024-09-05T20:53:41.762Z] =============================================== [2024-09-05T20:53:41.762Z] renaissance-movie-lens_0 Start Time: Thu Sep 5 20:53:41 2024 Epoch Time (ms): 1725569621243 [2024-09-05T20:53:41.762Z] variation: NoOptions [2024-09-05T20:53:41.762Z] JVM_OPTIONS: [2024-09-05T20:53:41.762Z] { \ [2024-09-05T20:53:41.762Z] echo ""; echo "TEST SETUP:"; \ [2024-09-05T20:53:41.762Z] echo "Nothing to be done for setup."; \ [2024-09-05T20:53:41.762Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17255689523030/renaissance-movie-lens_0"; \ [2024-09-05T20:53:41.762Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17255689523030/renaissance-movie-lens_0"; \ [2024-09-05T20:53:41.762Z] echo ""; echo "TESTING:"; \ [2024-09-05T20:53:41.762Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17255689523030/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-09-05T20:53:41.762Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17255689523030/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-09-05T20:53:41.762Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-09-05T20:53:41.762Z] echo "Nothing to be done for teardown."; \ [2024-09-05T20:53:41.762Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17255689523030/TestTargetResult"; [2024-09-05T20:53:41.762Z] [2024-09-05T20:53:41.763Z] TEST SETUP: [2024-09-05T20:53:41.763Z] Nothing to be done for setup. [2024-09-05T20:53:41.763Z] [2024-09-05T20:53:41.763Z] TESTING: [2024-09-05T20:53:44.740Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-09-05T20:53:46.665Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads. [2024-09-05T20:53:49.044Z] Got 100004 ratings from 671 users on 9066 movies. [2024-09-05T20:53:49.983Z] Training: 60056, validation: 20285, test: 19854 [2024-09-05T20:53:49.983Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-09-05T20:53:49.983Z] GC before operation: completed in 159.577 ms, heap usage 238.286 MB -> 29.426 MB. [2024-09-05T20:53:55.289Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:53:58.310Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:54:00.240Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:54:02.163Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:54:04.087Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:54:05.024Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:54:06.099Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:54:07.035Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:54:07.971Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:54:07.971Z] The best model improves the baseline by 14.43%. [2024-09-05T20:54:07.971Z] Movies recommended for you: [2024-09-05T20:54:07.971Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:54:07.971Z] There is no way to check that no silent failure occurred. [2024-09-05T20:54:07.971Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (18138.536 ms) ====== [2024-09-05T20:54:07.971Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-09-05T20:54:07.971Z] GC before operation: completed in 236.343 ms, heap usage 1.252 GB -> 54.536 MB. [2024-09-05T20:54:09.896Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:54:11.820Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:54:12.830Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:54:14.754Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:54:15.689Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:54:16.626Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:54:18.554Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:54:19.490Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:54:19.490Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-05T20:54:19.490Z] The best model improves the baseline by 14.43%. [2024-09-05T20:54:19.490Z] Movies recommended for you: [2024-09-05T20:54:19.491Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:54:19.491Z] There is no way to check that no silent failure occurred. [2024-09-05T20:54:19.491Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (11569.549 ms) ====== [2024-09-05T20:54:19.491Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-09-05T20:54:19.491Z] GC before operation: completed in 179.598 ms, heap usage 207.579 MB -> 45.164 MB. [2024-09-05T20:54:21.417Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:54:23.427Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:54:25.350Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:54:26.304Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:54:27.241Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:54:28.179Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:54:29.117Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:54:30.055Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:54:30.056Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-05T20:54:30.994Z] The best model improves the baseline by 14.43%. [2024-09-05T20:54:30.994Z] Movies recommended for you: [2024-09-05T20:54:30.994Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:54:30.994Z] There is no way to check that no silent failure occurred. [2024-09-05T20:54:30.994Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (10794.980 ms) ====== [2024-09-05T20:54:30.994Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-09-05T20:54:30.994Z] GC before operation: completed in 166.150 ms, heap usage 1.216 GB -> 53.676 MB. [2024-09-05T20:54:31.930Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:54:33.855Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:54:35.781Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:54:36.722Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:54:37.659Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:54:38.595Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:54:39.532Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:54:40.470Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:54:41.408Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-05T20:54:41.408Z] The best model improves the baseline by 14.43%. [2024-09-05T20:54:41.408Z] Movies recommended for you: [2024-09-05T20:54:41.408Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:54:41.408Z] There is no way to check that no silent failure occurred. [2024-09-05T20:54:41.408Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (10342.837 ms) ====== [2024-09-05T20:54:41.408Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-09-05T20:54:41.408Z] GC before operation: completed in 168.516 ms, heap usage 1.152 GB -> 53.588 MB. [2024-09-05T20:54:43.337Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:54:44.275Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:54:46.199Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:54:47.137Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:54:48.074Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:54:49.011Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:54:49.950Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:54:50.895Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:54:50.895Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:54:50.895Z] The best model improves the baseline by 14.43%. [2024-09-05T20:54:50.895Z] Movies recommended for you: [2024-09-05T20:54:50.895Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:54:50.895Z] There is no way to check that no silent failure occurred. [2024-09-05T20:54:50.895Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9995.049 ms) ====== [2024-09-05T20:54:50.895Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-09-05T20:54:51.831Z] GC before operation: completed in 157.069 ms, heap usage 1.253 GB -> 56.671 MB. [2024-09-05T20:54:52.780Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:54:54.707Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:54:55.649Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:54:57.578Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:54:58.516Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:54:59.452Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:55:00.389Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:55:02.040Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:55:02.040Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:55:02.040Z] The best model improves the baseline by 14.43%. [2024-09-05T20:55:02.040Z] Movies recommended for you: [2024-09-05T20:55:02.040Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:55:02.040Z] There is no way to check that no silent failure occurred. [2024-09-05T20:55:02.040Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (10014.964 ms) ====== [2024-09-05T20:55:02.040Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-09-05T20:55:02.040Z] GC before operation: completed in 148.875 ms, heap usage 147.694 MB -> 45.806 MB. [2024-09-05T20:55:02.977Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:55:04.899Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:55:05.835Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:55:07.756Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:55:08.691Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:55:09.627Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:55:10.562Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:55:11.507Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:55:11.507Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:55:11.507Z] The best model improves the baseline by 14.43%. [2024-09-05T20:55:11.507Z] Movies recommended for you: [2024-09-05T20:55:11.507Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:55:11.507Z] There is no way to check that no silent failure occurred. [2024-09-05T20:55:11.507Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (9839.530 ms) ====== [2024-09-05T20:55:11.507Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-09-05T20:55:11.507Z] GC before operation: completed in 138.467 ms, heap usage 1.144 GB -> 53.926 MB. [2024-09-05T20:55:13.430Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:55:14.367Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:55:16.289Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:55:17.227Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:55:18.161Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:55:19.097Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:55:20.032Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:55:20.968Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:55:20.968Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-05T20:55:20.968Z] The best model improves the baseline by 14.43%. [2024-09-05T20:55:20.968Z] Movies recommended for you: [2024-09-05T20:55:20.968Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:55:20.968Z] There is no way to check that no silent failure occurred. [2024-09-05T20:55:20.968Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (9722.828 ms) ====== [2024-09-05T20:55:20.968Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-09-05T20:55:21.907Z] GC before operation: completed in 142.648 ms, heap usage 1.167 GB -> 57.290 MB. [2024-09-05T20:55:22.842Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:55:24.783Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:55:25.722Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:55:27.648Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:55:28.584Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:55:29.518Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:55:30.454Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:55:31.389Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:55:31.389Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:55:31.389Z] The best model improves the baseline by 14.43%. [2024-09-05T20:55:31.389Z] Movies recommended for you: [2024-09-05T20:55:31.389Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:55:31.389Z] There is no way to check that no silent failure occurred. [2024-09-05T20:55:31.389Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (9912.821 ms) ====== [2024-09-05T20:55:31.389Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-09-05T20:55:31.389Z] GC before operation: completed in 136.132 ms, heap usage 1.236 GB -> 54.627 MB. [2024-09-05T20:55:33.312Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:55:34.247Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:55:36.175Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:55:37.118Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:55:38.054Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:55:38.990Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:55:39.927Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:55:40.862Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:55:41.798Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:55:41.798Z] The best model improves the baseline by 14.43%. [2024-09-05T20:55:41.798Z] Movies recommended for you: [2024-09-05T20:55:41.798Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:55:41.798Z] There is no way to check that no silent failure occurred. [2024-09-05T20:55:41.798Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (9947.292 ms) ====== [2024-09-05T20:55:41.798Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-09-05T20:55:41.798Z] GC before operation: completed in 169.731 ms, heap usage 1.173 GB -> 54.325 MB. [2024-09-05T20:55:42.736Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:55:44.657Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:55:46.595Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:55:47.529Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:55:48.464Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:55:49.401Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:55:50.338Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:55:51.274Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:55:51.274Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:55:51.274Z] The best model improves the baseline by 14.43%. [2024-09-05T20:55:51.274Z] Movies recommended for you: [2024-09-05T20:55:51.274Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:55:51.274Z] There is no way to check that no silent failure occurred. [2024-09-05T20:55:51.274Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (9931.590 ms) ====== [2024-09-05T20:55:51.274Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-09-05T20:55:51.274Z] GC before operation: completed in 158.839 ms, heap usage 1.208 GB -> 54.147 MB. [2024-09-05T20:55:53.194Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:55:55.136Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:55:56.073Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:55:57.997Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:55:57.997Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:55:58.931Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:55:59.868Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:56:00.803Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:56:00.803Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:56:00.803Z] The best model improves the baseline by 14.43%. [2024-09-05T20:56:00.803Z] Movies recommended for you: [2024-09-05T20:56:00.803Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:56:00.803Z] There is no way to check that no silent failure occurred. [2024-09-05T20:56:00.803Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (9520.900 ms) ====== [2024-09-05T20:56:00.803Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-09-05T20:56:01.739Z] GC before operation: completed in 123.950 ms, heap usage 1.165 GB -> 53.855 MB. [2024-09-05T20:56:02.676Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:56:04.596Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:56:05.532Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:56:07.452Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:56:08.389Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:56:10.019Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:56:10.968Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:56:10.968Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:56:10.968Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:56:10.968Z] The best model improves the baseline by 14.43%. [2024-09-05T20:56:10.968Z] Movies recommended for you: [2024-09-05T20:56:10.968Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:56:10.968Z] There is no way to check that no silent failure occurred. [2024-09-05T20:56:10.968Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (9673.190 ms) ====== [2024-09-05T20:56:10.968Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-09-05T20:56:10.968Z] GC before operation: completed in 143.771 ms, heap usage 1.203 GB -> 54.617 MB. [2024-09-05T20:56:12.894Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:56:13.831Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:56:15.751Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:56:16.688Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:56:17.625Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:56:18.561Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:56:19.496Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:56:20.431Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:56:20.431Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:56:20.431Z] The best model improves the baseline by 14.43%. [2024-09-05T20:56:20.431Z] Movies recommended for you: [2024-09-05T20:56:20.431Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:56:20.431Z] There is no way to check that no silent failure occurred. [2024-09-05T20:56:20.431Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (9693.976 ms) ====== [2024-09-05T20:56:20.431Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-09-05T20:56:21.369Z] GC before operation: completed in 119.532 ms, heap usage 1.213 GB -> 57.491 MB. [2024-09-05T20:56:22.316Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:56:24.241Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:56:25.177Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:56:27.109Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:56:28.046Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:56:28.983Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:56:29.922Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:56:30.859Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:56:30.859Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-05T20:56:30.859Z] The best model improves the baseline by 14.43%. [2024-09-05T20:56:30.859Z] Movies recommended for you: [2024-09-05T20:56:30.859Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:56:30.859Z] There is no way to check that no silent failure occurred. [2024-09-05T20:56:30.859Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (9664.469 ms) ====== [2024-09-05T20:56:30.859Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-09-05T20:56:30.859Z] GC before operation: completed in 142.161 ms, heap usage 1.302 GB -> 55.010 MB. [2024-09-05T20:56:32.790Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:56:33.727Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:56:35.652Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:56:36.589Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:56:37.523Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:56:38.459Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:56:39.396Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:56:40.349Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:56:40.349Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:56:40.349Z] The best model improves the baseline by 14.43%. [2024-09-05T20:56:40.349Z] Movies recommended for you: [2024-09-05T20:56:40.349Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:56:40.349Z] There is no way to check that no silent failure occurred. [2024-09-05T20:56:40.349Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (9712.351 ms) ====== [2024-09-05T20:56:40.349Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-09-05T20:56:40.349Z] GC before operation: completed in 133.397 ms, heap usage 1.210 GB -> 54.712 MB. [2024-09-05T20:56:42.271Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:56:43.211Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:56:45.134Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:56:46.072Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:56:47.009Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:56:47.948Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:56:48.884Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:56:49.820Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:56:49.820Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:56:49.820Z] The best model improves the baseline by 14.43%. [2024-09-05T20:56:49.820Z] Movies recommended for you: [2024-09-05T20:56:49.820Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:56:49.820Z] There is no way to check that no silent failure occurred. [2024-09-05T20:56:49.820Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (9542.345 ms) ====== [2024-09-05T20:56:49.820Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-09-05T20:56:50.757Z] GC before operation: completed in 154.294 ms, heap usage 1.248 GB -> 54.777 MB. [2024-09-05T20:56:51.696Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:56:53.616Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:56:54.554Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:56:56.474Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:56:57.409Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:56:58.345Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:56:59.281Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:57:00.217Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:57:00.217Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:57:00.217Z] The best model improves the baseline by 14.43%. [2024-09-05T20:57:00.217Z] Movies recommended for you: [2024-09-05T20:57:00.217Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:57:00.217Z] There is no way to check that no silent failure occurred. [2024-09-05T20:57:00.217Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (9776.765 ms) ====== [2024-09-05T20:57:00.217Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-09-05T20:57:00.217Z] GC before operation: completed in 153.192 ms, heap usage 1.236 GB -> 57.019 MB. [2024-09-05T20:57:02.144Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:57:03.081Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:57:05.004Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:57:05.942Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:57:06.878Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:57:07.814Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:57:08.750Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:57:09.696Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:57:09.696Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:57:09.696Z] The best model improves the baseline by 14.43%. [2024-09-05T20:57:09.696Z] Movies recommended for you: [2024-09-05T20:57:09.696Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:57:09.696Z] There is no way to check that no silent failure occurred. [2024-09-05T20:57:09.696Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (9570.262 ms) ====== [2024-09-05T20:57:09.696Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-09-05T20:57:09.696Z] GC before operation: completed in 145.952 ms, heap usage 1.232 GB -> 54.110 MB. [2024-09-05T20:57:11.622Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-05T20:57:12.572Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-05T20:57:14.511Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-05T20:57:15.447Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-05T20:57:16.391Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-05T20:57:17.333Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-05T20:57:18.972Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-05T20:57:18.972Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-05T20:57:19.910Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-05T20:57:19.910Z] The best model improves the baseline by 14.43%. [2024-09-05T20:57:19.910Z] Movies recommended for you: [2024-09-05T20:57:19.910Z] WARNING: This benchmark provides no result that can be validated. [2024-09-05T20:57:19.910Z] There is no way to check that no silent failure occurred. [2024-09-05T20:57:19.910Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (9574.957 ms) ====== [2024-09-05T20:57:20.845Z] ----------------------------------- [2024-09-05T20:57:20.845Z] renaissance-movie-lens_0_PASSED [2024-09-05T20:57:20.845Z] ----------------------------------- [2024-09-05T20:57:20.845Z] [2024-09-05T20:57:20.845Z] TEST TEARDOWN: [2024-09-05T20:57:20.845Z] Nothing to be done for teardown. [2024-09-05T20:57:20.845Z] renaissance-movie-lens_0 Finish Time: Thu Sep 5 20:57:19 2024 Epoch Time (ms): 1725569839968