renaissance-movie-lens_0
[2024-11-15T23:46:39.477Z] Running test renaissance-movie-lens_0 ...
[2024-11-15T23:46:39.477Z] ===============================================
[2024-11-15T23:46:39.477Z] renaissance-movie-lens_0 Start Time: Fri Nov 15 23:46:38 2024 Epoch Time (ms): 1731714398849
[2024-11-15T23:46:39.477Z] variation: NoOptions
[2024-11-15T23:46:39.477Z] JVM_OPTIONS:
[2024-11-15T23:46:39.477Z] { \
[2024-11-15T23:46:39.477Z] echo ""; echo "TEST SETUP:"; \
[2024-11-15T23:46:39.477Z] echo "Nothing to be done for setup."; \
[2024-11-15T23:46:39.477Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317134666610/renaissance-movie-lens_0"; \
[2024-11-15T23:46:39.477Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317134666610/renaissance-movie-lens_0"; \
[2024-11-15T23:46:39.477Z] echo ""; echo "TESTING:"; \
[2024-11-15T23:46:39.477Z] "/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_17317134666610/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-15T23:46:39.477Z] 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_17317134666610/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-15T23:46:39.477Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-15T23:46:39.477Z] echo "Nothing to be done for teardown."; \
[2024-11-15T23:46:39.477Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317134666610/TestTargetResult";
[2024-11-15T23:46:39.477Z]
[2024-11-15T23:46:39.477Z] TEST SETUP:
[2024-11-15T23:46:39.477Z] Nothing to be done for setup.
[2024-11-15T23:46:39.477Z]
[2024-11-15T23:46:39.477Z] TESTING:
[2024-11-15T23:46:43.524Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-15T23:46:46.465Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-15T23:46:51.698Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-15T23:46:51.698Z] Training: 60056, validation: 20285, test: 19854
[2024-11-15T23:46:51.698Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-15T23:46:51.698Z] GC before operation: completed in 89.236 ms, heap usage 170.979 MB -> 37.182 MB.
[2024-11-15T23:47:00.334Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:47:05.614Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:47:08.685Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:47:11.621Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:47:13.526Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:47:15.427Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:47:17.344Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:47:19.244Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:47:20.178Z] 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.
[2024-11-15T23:47:20.178Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:47:20.178Z] Movies recommended for you:
[2024-11-15T23:47:20.178Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:47:20.178Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:47:20.178Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (28257.837 ms) ======
[2024-11-15T23:47:20.178Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-15T23:47:20.178Z] GC before operation: completed in 146.283 ms, heap usage 528.551 MB -> 54.316 MB.
[2024-11-15T23:47:23.116Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:47:26.054Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:47:29.042Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:47:32.029Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:47:33.934Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:47:35.835Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:47:37.743Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:47:39.645Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:47:39.646Z] 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.
[2024-11-15T23:47:39.646Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:47:39.646Z] Movies recommended for you:
[2024-11-15T23:47:39.646Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:47:39.646Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:47:39.646Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (19653.438 ms) ======
[2024-11-15T23:47:39.646Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-15T23:47:39.646Z] GC before operation: completed in 181.781 ms, heap usage 257.562 MB -> 50.950 MB.
[2024-11-15T23:47:42.580Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:47:45.521Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:47:48.461Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:47:51.399Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:47:53.000Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:47:54.902Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:47:55.827Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:47:57.732Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:47:57.732Z] 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.
[2024-11-15T23:47:57.732Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:47:57.732Z] Movies recommended for you:
[2024-11-15T23:47:57.732Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:47:57.732Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:47:57.732Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17988.723 ms) ======
[2024-11-15T23:47:57.732Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-15T23:47:58.658Z] GC before operation: completed in 166.480 ms, heap usage 563.832 MB -> 54.789 MB.
[2024-11-15T23:48:00.558Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:48:03.495Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:48:06.436Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:48:08.337Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:48:10.239Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:48:11.164Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:48:13.066Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:48:13.993Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:48:14.921Z] 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.
[2024-11-15T23:48:14.921Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:48:14.921Z] Movies recommended for you:
[2024-11-15T23:48:14.921Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:48:14.921Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:48:14.921Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16614.850 ms) ======
[2024-11-15T23:48:14.921Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-15T23:48:14.921Z] GC before operation: completed in 208.756 ms, heap usage 336.980 MB -> 51.804 MB.
[2024-11-15T23:48:17.859Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:48:19.760Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:48:22.698Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:48:24.599Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:48:26.503Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:48:27.429Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:48:29.333Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:48:31.433Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:48:31.433Z] 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.
[2024-11-15T23:48:31.433Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:48:31.433Z] Movies recommended for you:
[2024-11-15T23:48:31.433Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:48:31.433Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:48:31.433Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16226.808 ms) ======
[2024-11-15T23:48:31.433Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-15T23:48:31.433Z] GC before operation: completed in 179.398 ms, heap usage 264.628 MB -> 51.881 MB.
[2024-11-15T23:48:34.370Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:48:36.272Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:48:39.210Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:48:41.119Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:48:43.028Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:48:43.955Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:48:45.857Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:48:46.785Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:48:47.712Z] 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.
[2024-11-15T23:48:47.712Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:48:47.712Z] Movies recommended for you:
[2024-11-15T23:48:47.712Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:48:47.712Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:48:47.712Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16003.353 ms) ======
[2024-11-15T23:48:47.712Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-15T23:48:47.712Z] GC before operation: completed in 166.424 ms, heap usage 566.924 MB -> 55.310 MB.
[2024-11-15T23:48:49.615Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:48:52.201Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:48:55.150Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:48:57.054Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:48:58.957Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:49:00.860Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:49:01.789Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:49:03.694Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:49:03.695Z] 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.
[2024-11-15T23:49:03.695Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:49:03.695Z] Movies recommended for you:
[2024-11-15T23:49:03.695Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:49:03.695Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:49:03.695Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16095.006 ms) ======
[2024-11-15T23:49:03.695Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-15T23:49:03.695Z] GC before operation: completed in 168.225 ms, heap usage 559.850 MB -> 55.428 MB.
[2024-11-15T23:49:06.637Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:49:08.542Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:49:11.483Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:49:13.383Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:49:15.288Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:49:16.214Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:49:18.119Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:49:20.029Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:49:20.029Z] 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.
[2024-11-15T23:49:20.029Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:49:20.029Z] Movies recommended for you:
[2024-11-15T23:49:20.029Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:49:20.029Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:49:20.029Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15960.913 ms) ======
[2024-11-15T23:49:20.029Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-15T23:49:20.029Z] GC before operation: completed in 169.065 ms, heap usage 541.918 MB -> 55.627 MB.
[2024-11-15T23:49:22.971Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:49:24.874Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:49:27.812Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:49:29.715Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:49:31.625Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:49:32.553Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:49:34.455Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:49:35.382Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:49:35.382Z] 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.
[2024-11-15T23:49:36.307Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:49:36.307Z] Movies recommended for you:
[2024-11-15T23:49:36.307Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:49:36.307Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:49:36.307Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15949.183 ms) ======
[2024-11-15T23:49:36.307Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-15T23:49:36.307Z] GC before operation: completed in 166.708 ms, heap usage 567.298 MB -> 55.479 MB.
[2024-11-15T23:49:38.210Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:49:41.151Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:49:43.056Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:49:45.995Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:49:46.922Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:49:49.498Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:49:50.424Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:49:51.350Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:49:52.275Z] 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.
[2024-11-15T23:49:52.275Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:49:52.275Z] Movies recommended for you:
[2024-11-15T23:49:52.275Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:49:52.275Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:49:52.275Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15892.732 ms) ======
[2024-11-15T23:49:52.275Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-15T23:49:52.275Z] GC before operation: completed in 176.756 ms, heap usage 228.287 MB -> 52.123 MB.
[2024-11-15T23:49:54.176Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:49:57.114Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:49:59.013Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:50:01.946Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:50:02.871Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:50:04.771Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:50:05.700Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:50:07.600Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:50:07.600Z] 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.
[2024-11-15T23:50:07.600Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:50:07.600Z] Movies recommended for you:
[2024-11-15T23:50:07.600Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:50:07.600Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:50:07.600Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15622.408 ms) ======
[2024-11-15T23:50:07.600Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-15T23:50:07.601Z] GC before operation: completed in 172.417 ms, heap usage 565.255 MB -> 55.307 MB.
[2024-11-15T23:50:10.548Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:50:13.502Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:50:15.406Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:50:17.316Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:50:19.226Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:50:20.152Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:50:22.063Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:50:23.975Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:50:23.975Z] 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.
[2024-11-15T23:50:23.975Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:50:23.975Z] Movies recommended for you:
[2024-11-15T23:50:23.975Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:50:23.975Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:50:23.975Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15815.550 ms) ======
[2024-11-15T23:50:23.975Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-15T23:50:23.975Z] GC before operation: completed in 182.377 ms, heap usage 598.153 MB -> 55.553 MB.
[2024-11-15T23:50:26.916Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:50:28.821Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:50:31.763Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:50:33.669Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:50:35.577Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:50:37.482Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:50:38.408Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:50:40.311Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:50:40.311Z] 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.
[2024-11-15T23:50:40.311Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:50:40.311Z] Movies recommended for you:
[2024-11-15T23:50:40.311Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:50:40.311Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:50:40.311Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16518.480 ms) ======
[2024-11-15T23:50:40.311Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-15T23:50:40.311Z] GC before operation: completed in 177.097 ms, heap usage 541.750 MB -> 55.714 MB.
[2024-11-15T23:50:43.256Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:50:45.160Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:50:48.479Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:50:50.389Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:50:52.293Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:50:53.223Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:50:55.127Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:50:56.054Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:50:56.983Z] 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.
[2024-11-15T23:50:56.983Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:50:56.983Z] Movies recommended for you:
[2024-11-15T23:50:56.983Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:50:56.983Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:50:56.983Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16123.821 ms) ======
[2024-11-15T23:50:56.983Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-15T23:50:56.983Z] GC before operation: completed in 176.458 ms, heap usage 630.501 MB -> 55.546 MB.
[2024-11-15T23:50:59.923Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:01.830Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:04.768Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:06.678Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:07.732Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:09.633Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:10.730Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:51:12.633Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:51:12.633Z] 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.
[2024-11-15T23:51:12.633Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:51:12.633Z] Movies recommended for you:
[2024-11-15T23:51:12.633Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:51:12.633Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:51:12.633Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15775.741 ms) ======
[2024-11-15T23:51:12.633Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-15T23:51:12.633Z] GC before operation: completed in 177.627 ms, heap usage 217.801 MB -> 52.203 MB.
[2024-11-15T23:51:15.573Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:17.475Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:20.416Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:22.319Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:24.226Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:25.160Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:27.063Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:51:28.972Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:51:28.972Z] 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.
[2024-11-15T23:51:28.972Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:51:28.972Z] Movies recommended for you:
[2024-11-15T23:51:28.972Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:51:28.972Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:51:28.972Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15937.006 ms) ======
[2024-11-15T23:51:28.972Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-15T23:51:28.972Z] GC before operation: completed in 190.989 ms, heap usage 502.953 MB -> 52.410 MB.
[2024-11-15T23:51:31.913Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:33.816Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:36.756Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:38.660Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:39.586Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:41.489Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:43.392Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:51:44.326Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:51:44.326Z] 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.
[2024-11-15T23:51:44.326Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:51:44.326Z] Movies recommended for you:
[2024-11-15T23:51:44.326Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:51:44.326Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:51:44.326Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15575.398 ms) ======
[2024-11-15T23:51:44.326Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-15T23:51:45.921Z] GC before operation: completed in 179.490 ms, heap usage 543.818 MB -> 55.578 MB.
[2024-11-15T23:51:46.847Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:51:49.790Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:51:51.699Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:51:54.641Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:51:55.573Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:51:57.477Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:51:59.383Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:52:00.311Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:52:00.311Z] 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.
[2024-11-15T23:52:00.311Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:52:01.237Z] Movies recommended for you:
[2024-11-15T23:52:01.237Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:52:01.237Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:52:01.237Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15939.444 ms) ======
[2024-11-15T23:52:01.237Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-15T23:52:01.237Z] GC before operation: completed in 191.076 ms, heap usage 72.141 MB -> 54.690 MB.
[2024-11-15T23:52:03.139Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:52:06.081Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:52:07.986Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:52:10.931Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:52:11.859Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:52:13.763Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:52:14.691Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:52:16.597Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:52:16.597Z] 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.
[2024-11-15T23:52:16.597Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:52:16.597Z] Movies recommended for you:
[2024-11-15T23:52:16.597Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:52:16.597Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:52:16.597Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15945.073 ms) ======
[2024-11-15T23:52:16.597Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-15T23:52:17.525Z] GC before operation: completed in 180.109 ms, heap usage 563.163 MB -> 55.847 MB.
[2024-11-15T23:52:19.430Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T23:52:22.374Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T23:52:24.278Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T23:52:27.221Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T23:52:28.150Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T23:52:30.065Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T23:52:31.018Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T23:52:32.927Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T23:52:32.927Z] 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.
[2024-11-15T23:52:32.927Z] The best model improves the baseline by 14.43%.
[2024-11-15T23:52:32.927Z] Movies recommended for you:
[2024-11-15T23:52:32.927Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T23:52:32.927Z] There is no way to check that no silent failure occurred.
[2024-11-15T23:52:32.927Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (15951.389 ms) ======
[2024-11-15T23:52:34.857Z] -----------------------------------
[2024-11-15T23:52:34.857Z] renaissance-movie-lens_0_PASSED
[2024-11-15T23:52:34.857Z] -----------------------------------
[2024-11-15T23:52:34.857Z]
[2024-11-15T23:52:34.857Z] TEST TEARDOWN:
[2024-11-15T23:52:34.857Z] Nothing to be done for teardown.
[2024-11-15T23:52:34.857Z] renaissance-movie-lens_0 Finish Time: Fri Nov 15 23:52:34 2024 Epoch Time (ms): 1731714754229