[2023-04-19T00:08:57.570Z] Running test renaissance-movie-lens_0 ... [2023-04-19T00:08:57.570Z] =============================================== [2023-04-19T00:08:57.570Z] renaissance-movie-lens_0 Start Time: Tue Apr 18 19:08:54 2023 Epoch Time (ms): 1681862934069 [2023-04-19T00:08:58.112Z] variation: NoOptions [2023-04-19T00:08:58.112Z] JVM_OPTIONS: [2023-04-19T00:08:58.112Z] { \ [2023-04-19T00:08:58.112Z] echo ""; echo "TEST SETUP:"; \ [2023-04-19T00:08:58.112Z] echo "Nothing to be done for setup."; \ [2023-04-19T00:08:58.112Z] mkdir -p "E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16818620773696\\renaissance-movie-lens_0"; \ [2023-04-19T00:08:58.112Z] cd "E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16818620773696\\renaissance-movie-lens_0"; \ [2023-04-19T00:08:58.112Z] echo ""; echo "TESTING:"; \ [2023-04-19T00:08:58.113Z] "E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/openjdkbinary/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 "E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16818620773696\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2023-04-19T00:08:58.113Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/; rm -f -r "E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16818620773696\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2023-04-19T00:08:58.113Z] echo ""; echo "TEST TEARDOWN:"; \ [2023-04-19T00:08:58.113Z] echo "Nothing to be done for teardown."; \ [2023-04-19T00:08:58.113Z] } 2>&1 | tee -a "E:/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16818620773696\\TestTargetResult"; [2023-04-19T00:08:58.113Z] [2023-04-19T00:08:58.113Z] TEST SETUP: [2023-04-19T00:08:58.113Z] Nothing to be done for setup. [2023-04-19T00:08:58.113Z] [2023-04-19T00:08:58.113Z] TESTING: [2023-04-19T00:09:01.441Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2023-04-19T00:09:06.713Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads. [2023-04-19T00:09:11.992Z] Got 100004 ratings from 671 users on 9066 movies. [2023-04-19T00:09:12.535Z] Training: 60056, validation: 20285, test: 19854 [2023-04-19T00:09:12.535Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2023-04-19T00:09:12.535Z] GC before operation: completed in 65.358 ms, heap usage 101.167 MB -> 38.159 MB. [2023-04-19T00:09:21.942Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:09:26.178Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:09:30.417Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:09:34.649Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:09:37.154Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:09:39.662Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:09:41.544Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:09:44.094Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:09:44.636Z] 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. [2023-04-19T00:09:44.636Z] The best model improves the baseline by 14.43%. [2023-04-19T00:09:44.636Z] Movies recommended for you: [2023-04-19T00:09:44.636Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:09:44.636Z] There is no way to check that no silent failure occurred. [2023-04-19T00:09:44.636Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (32319.331 ms) ====== [2023-04-19T00:09:44.636Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2023-04-19T00:09:44.636Z] GC before operation: completed in 109.374 ms, heap usage 534.298 MB -> 62.896 MB. [2023-04-19T00:09:49.915Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:09:53.235Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:09:58.504Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:10:01.831Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:10:04.336Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:10:06.844Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:10:08.628Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:10:11.950Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:10:11.950Z] 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. [2023-04-19T00:10:11.950Z] The best model improves the baseline by 14.43%. [2023-04-19T00:10:11.950Z] Movies recommended for you: [2023-04-19T00:10:11.950Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:10:11.950Z] There is no way to check that no silent failure occurred. [2023-04-19T00:10:11.950Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (27241.506 ms) ====== [2023-04-19T00:10:11.950Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2023-04-19T00:10:11.950Z] GC before operation: completed in 70.808 ms, heap usage 195.374 MB -> 51.999 MB. [2023-04-19T00:10:16.179Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:10:20.414Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:10:24.909Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:10:29.137Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:10:30.919Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:10:33.442Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:10:35.952Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:10:38.462Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:10:38.462Z] 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. [2023-04-19T00:10:38.462Z] The best model improves the baseline by 14.43%. [2023-04-19T00:10:38.462Z] Movies recommended for you: [2023-04-19T00:10:38.462Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:10:38.462Z] There is no way to check that no silent failure occurred. [2023-04-19T00:10:38.462Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (26482.569 ms) ====== [2023-04-19T00:10:38.462Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2023-04-19T00:10:39.005Z] GC before operation: completed in 84.669 ms, heap usage 510.916 MB -> 55.860 MB. [2023-04-19T00:10:43.238Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:10:47.463Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:10:51.699Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:10:55.018Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:10:57.523Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:11:00.033Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:11:02.544Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:11:05.056Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:11:05.056Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2023-04-19T00:11:05.056Z] The best model improves the baseline by 14.43%. [2023-04-19T00:11:05.602Z] Movies recommended for you: [2023-04-19T00:11:05.602Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:11:05.602Z] There is no way to check that no silent failure occurred. [2023-04-19T00:11:05.602Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (26634.392 ms) ====== [2023-04-19T00:11:05.602Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2023-04-19T00:11:05.602Z] GC before operation: completed in 66.405 ms, heap usage 297.918 MB -> 52.797 MB. [2023-04-19T00:11:09.832Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:11:14.071Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:11:18.307Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:11:22.541Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:11:25.044Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:11:27.579Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:11:29.367Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:11:31.874Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:11:31.874Z] 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. [2023-04-19T00:11:31.874Z] The best model improves the baseline by 14.43%. [2023-04-19T00:11:32.425Z] Movies recommended for you: [2023-04-19T00:11:32.425Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:11:32.425Z] There is no way to check that no silent failure occurred. [2023-04-19T00:11:32.425Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (26755.661 ms) ====== [2023-04-19T00:11:32.425Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2023-04-19T00:11:32.425Z] GC before operation: completed in 63.885 ms, heap usage 198.401 MB -> 53.029 MB. [2023-04-19T00:11:36.667Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:11:40.962Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:11:45.202Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:11:48.518Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:11:51.026Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:11:53.538Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:11:56.049Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:11:58.560Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:11:59.101Z] 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. [2023-04-19T00:11:59.101Z] The best model improves the baseline by 14.43%. [2023-04-19T00:11:59.101Z] Movies recommended for you: [2023-04-19T00:11:59.101Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:11:59.101Z] There is no way to check that no silent failure occurred. [2023-04-19T00:11:59.101Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (27013.215 ms) ====== [2023-04-19T00:11:59.101Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2023-04-19T00:11:59.101Z] GC before operation: completed in 68.798 ms, heap usage 182.675 MB -> 52.811 MB. [2023-04-19T00:12:04.368Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:12:08.598Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:12:11.909Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:12:16.137Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:12:18.653Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:12:21.182Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:12:23.693Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:12:25.480Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:12:26.023Z] 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. [2023-04-19T00:12:26.024Z] The best model improves the baseline by 14.43%. [2023-04-19T00:12:26.024Z] Movies recommended for you: [2023-04-19T00:12:26.024Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:12:26.024Z] There is no way to check that no silent failure occurred. [2023-04-19T00:12:26.024Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (26787.050 ms) ====== [2023-04-19T00:12:26.024Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2023-04-19T00:12:26.024Z] GC before operation: completed in 64.638 ms, heap usage 586.180 MB -> 56.537 MB. [2023-04-19T00:12:30.262Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:12:34.530Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:12:38.764Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:12:43.001Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:12:45.518Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:12:47.304Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:12:49.811Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:12:52.321Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:12:52.321Z] 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. [2023-04-19T00:12:52.865Z] The best model improves the baseline by 14.43%. [2023-04-19T00:12:52.865Z] Movies recommended for you: [2023-04-19T00:12:52.865Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:12:52.865Z] There is no way to check that no silent failure occurred. [2023-04-19T00:12:52.865Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (26526.759 ms) ====== [2023-04-19T00:12:52.865Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2023-04-19T00:12:52.865Z] GC before operation: completed in 68.381 ms, heap usage 302.695 MB -> 53.331 MB. [2023-04-19T00:12:57.105Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:01.344Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:05.577Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:09.815Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:12.336Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:14.850Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:16.636Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:19.149Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:19.149Z] 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. [2023-04-19T00:13:19.149Z] The best model improves the baseline by 14.43%. [2023-04-19T00:13:19.689Z] Movies recommended for you: [2023-04-19T00:13:19.689Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:19.689Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:19.689Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (26830.153 ms) ====== [2023-04-19T00:13:19.689Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2023-04-19T00:13:19.689Z] GC before operation: completed in 65.817 ms, heap usage 214.522 MB -> 53.096 MB. [2023-04-19T00:13:23.925Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:28.152Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:32.389Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:13:36.636Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:13:38.420Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:13:40.205Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:13:42.714Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:13:45.245Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:13:45.245Z] 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. [2023-04-19T00:13:45.245Z] The best model improves the baseline by 14.43%. [2023-04-19T00:13:45.245Z] Movies recommended for you: [2023-04-19T00:13:45.245Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:13:45.245Z] There is no way to check that no silent failure occurred. [2023-04-19T00:13:45.245Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (25803.426 ms) ====== [2023-04-19T00:13:45.245Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2023-04-19T00:13:45.788Z] GC before operation: completed in 66.752 ms, heap usage 483.155 MB -> 56.703 MB. [2023-04-19T00:13:50.028Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:13:54.258Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:13:58.705Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:14:02.020Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:14:04.537Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:14:07.046Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:14:09.556Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:14:12.060Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:14:12.060Z] 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. [2023-04-19T00:14:12.060Z] The best model improves the baseline by 14.43%. [2023-04-19T00:14:12.060Z] Movies recommended for you: [2023-04-19T00:14:12.060Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:14:12.060Z] There is no way to check that no silent failure occurred. [2023-04-19T00:14:12.060Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (26448.996 ms) ====== [2023-04-19T00:14:12.060Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2023-04-19T00:14:12.060Z] GC before operation: completed in 66.197 ms, heap usage 572.333 MB -> 56.495 MB. [2023-04-19T00:14:16.287Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:14:20.521Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:14:24.751Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:14:28.988Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:14:31.499Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:14:34.011Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:14:36.522Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:14:39.037Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:14:39.037Z] 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. [2023-04-19T00:14:39.037Z] The best model improves the baseline by 14.43%. [2023-04-19T00:14:39.581Z] Movies recommended for you: [2023-04-19T00:14:39.581Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:14:39.581Z] There is no way to check that no silent failure occurred. [2023-04-19T00:14:39.581Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (27412.989 ms) ====== [2023-04-19T00:14:39.581Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2023-04-19T00:14:39.581Z] GC before operation: completed in 75.213 ms, heap usage 224.647 MB -> 53.136 MB. [2023-04-19T00:14:43.913Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:14:48.186Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:14:52.424Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:14:56.653Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:14:58.443Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:15:00.948Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:15:03.457Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:15:05.968Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:15:06.511Z] 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. [2023-04-19T00:15:06.511Z] The best model improves the baseline by 14.43%. [2023-04-19T00:15:06.511Z] Movies recommended for you: [2023-04-19T00:15:06.511Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:15:06.511Z] There is no way to check that no silent failure occurred. [2023-04-19T00:15:06.511Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (26996.552 ms) ====== [2023-04-19T00:15:06.511Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2023-04-19T00:15:06.511Z] GC before operation: completed in 68.718 ms, heap usage 290.103 MB -> 53.402 MB. [2023-04-19T00:15:10.873Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:15:15.098Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:15:19.341Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:15:22.666Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:15:25.175Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:15:26.963Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:15:30.287Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:15:32.078Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:15:32.078Z] 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. [2023-04-19T00:15:32.619Z] The best model improves the baseline by 14.43%. [2023-04-19T00:15:32.619Z] Movies recommended for you: [2023-04-19T00:15:32.619Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:15:32.619Z] There is no way to check that no silent failure occurred. [2023-04-19T00:15:32.619Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (25859.906 ms) ====== [2023-04-19T00:15:32.619Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2023-04-19T00:15:32.619Z] GC before operation: completed in 71.254 ms, heap usage 587.434 MB -> 56.571 MB. [2023-04-19T00:15:36.861Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:15:41.106Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:15:45.353Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:15:49.610Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:15:51.390Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:15:53.898Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:15:56.432Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:15:58.219Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:15:58.761Z] 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. [2023-04-19T00:15:58.761Z] The best model improves the baseline by 14.43%. [2023-04-19T00:15:58.762Z] Movies recommended for you: [2023-04-19T00:15:58.762Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:15:58.762Z] There is no way to check that no silent failure occurred. [2023-04-19T00:15:58.762Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (26383.929 ms) ====== [2023-04-19T00:15:58.762Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2023-04-19T00:15:58.762Z] GC before operation: completed in 72.601 ms, heap usage 219.977 MB -> 53.285 MB. [2023-04-19T00:16:02.991Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:07.215Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:11.443Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:14.762Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:17.274Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:19.783Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:22.312Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:24.091Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:24.634Z] 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. [2023-04-19T00:16:24.635Z] The best model improves the baseline by 14.43%. [2023-04-19T00:16:24.635Z] Movies recommended for you: [2023-04-19T00:16:24.635Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:24.635Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:24.635Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (25821.470 ms) ====== [2023-04-19T00:16:24.635Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2023-04-19T00:16:25.177Z] GC before operation: completed in 69.265 ms, heap usage 234.265 MB -> 53.348 MB. [2023-04-19T00:16:29.398Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:33.626Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:16:37.865Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:16:41.417Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:16:43.933Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:16:46.452Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:16:48.962Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:16:50.747Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:16:51.288Z] 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. [2023-04-19T00:16:51.288Z] The best model improves the baseline by 14.43%. [2023-04-19T00:16:51.288Z] Movies recommended for you: [2023-04-19T00:16:51.288Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:16:51.288Z] There is no way to check that no silent failure occurred. [2023-04-19T00:16:51.288Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (26496.891 ms) ====== [2023-04-19T00:16:51.288Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2023-04-19T00:16:51.288Z] GC before operation: completed in 67.398 ms, heap usage 266.057 MB -> 53.272 MB. [2023-04-19T00:16:56.562Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:16:59.882Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:17:04.142Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:17:08.365Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:17:10.868Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:17:13.376Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:17:15.159Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:17:17.669Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:17:17.669Z] 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. [2023-04-19T00:17:17.669Z] The best model improves the baseline by 14.43%. [2023-04-19T00:17:17.669Z] Movies recommended for you: [2023-04-19T00:17:17.669Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:17:17.669Z] There is no way to check that no silent failure occurred. [2023-04-19T00:17:17.669Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (26430.032 ms) ====== [2023-04-19T00:17:17.669Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2023-04-19T00:17:18.210Z] GC before operation: completed in 64.742 ms, heap usage 471.844 MB -> 56.761 MB. [2023-04-19T00:17:22.442Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:17:26.672Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:17:30.904Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:17:35.240Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:17:37.761Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:17:40.271Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:17:42.785Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:17:44.568Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:17:45.110Z] 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. [2023-04-19T00:17:45.110Z] The best model improves the baseline by 14.43%. [2023-04-19T00:17:45.110Z] Movies recommended for you: [2023-04-19T00:17:45.110Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:17:45.110Z] There is no way to check that no silent failure occurred. [2023-04-19T00:17:45.110Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (27370.425 ms) ====== [2023-04-19T00:17:45.110Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2023-04-19T00:17:45.655Z] GC before operation: completed in 71.733 ms, heap usage 182.182 MB -> 53.404 MB. [2023-04-19T00:17:49.887Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T00:17:54.121Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T00:17:58.351Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T00:18:02.616Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T00:18:04.398Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T00:18:06.905Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T00:18:08.693Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T00:18:11.200Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T00:18:11.200Z] 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. [2023-04-19T00:18:11.200Z] The best model improves the baseline by 14.43%. [2023-04-19T00:18:11.200Z] Movies recommended for you: [2023-04-19T00:18:11.200Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T00:18:11.200Z] There is no way to check that no silent failure occurred. [2023-04-19T00:18:11.200Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (25895.541 ms) ====== [2023-04-19T00:18:12.333Z] ----------------------------------- [2023-04-19T00:18:12.333Z] renaissance-movie-lens_0_PASSED [2023-04-19T00:18:12.333Z] ----------------------------------- [2023-04-19T00:18:12.881Z] [2023-04-19T00:18:12.881Z] TEST TEARDOWN: [2023-04-19T00:18:12.881Z] Nothing to be done for teardown. [2023-04-19T00:18:12.881Z] renaissance-movie-lens_0 Finish Time: Tue Apr 18 19:18:09 2023 Epoch Time (ms): 1681863489050