renaissance-movie-lens_0
[2024-11-27T22:12:55.383Z] Running test renaissance-movie-lens_0 ...
[2024-11-27T22:12:55.383Z] ===============================================
[2024-11-27T22:12:55.383Z] renaissance-movie-lens_0 Start Time: Wed Nov 27 22:12:54 2024 Epoch Time (ms): 1732745574545
[2024-11-27T22:12:55.383Z] variation: NoOptions
[2024-11-27T22:12:55.383Z] JVM_OPTIONS:
[2024-11-27T22:12:55.383Z] { \
[2024-11-27T22:12:55.383Z] echo ""; echo "TEST SETUP:"; \
[2024-11-27T22:12:55.383Z] echo "Nothing to be done for setup."; \
[2024-11-27T22:12:55.383Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17327448368365/renaissance-movie-lens_0"; \
[2024-11-27T22:12:55.383Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17327448368365/renaissance-movie-lens_0"; \
[2024-11-27T22:12:55.383Z] echo ""; echo "TESTING:"; \
[2024-11-27T22:12:55.383Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17327448368365/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-27T22:12:55.383Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17327448368365/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-27T22:12:55.383Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-27T22:12:55.383Z] echo "Nothing to be done for teardown."; \
[2024-11-27T22:12:55.383Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17327448368365/TestTargetResult";
[2024-11-27T22:12:55.383Z]
[2024-11-27T22:12:55.383Z] TEST SETUP:
[2024-11-27T22:12:55.383Z] Nothing to be done for setup.
[2024-11-27T22:12:55.383Z]
[2024-11-27T22:12:55.383Z] TESTING:
[2024-11-27T22:12:58.372Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-27T22:13:00.321Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-27T22:13:04.436Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-27T22:13:04.436Z] Training: 60056, validation: 20285, test: 19854
[2024-11-27T22:13:04.436Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-27T22:13:04.436Z] GC before operation: completed in 64.924 ms, heap usage 61.842 MB -> 39.536 MB.
[2024-11-27T22:13:11.082Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:13:15.205Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:13:17.848Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:13:20.841Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:13:21.783Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:13:23.754Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:13:24.697Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:13:26.632Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:13:26.632Z] 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.
[2024-11-27T22:13:26.632Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:13:26.632Z] Movies recommended for you:
[2024-11-27T22:13:26.632Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:13:26.632Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:13:26.632Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (22410.503 ms) ======
[2024-11-27T22:13:26.632Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-27T22:13:27.575Z] GC before operation: completed in 115.387 ms, heap usage 474.347 MB -> 52.520 MB.
[2024-11-27T22:13:29.513Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:13:32.548Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:13:34.485Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:13:37.474Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:13:38.416Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:13:39.364Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:13:41.302Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:13:42.244Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:13:42.245Z] 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.
[2024-11-27T22:13:42.245Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:13:42.245Z] Movies recommended for you:
[2024-11-27T22:13:42.245Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:13:42.245Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:13:42.245Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15493.892 ms) ======
[2024-11-27T22:13:42.245Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-27T22:13:43.190Z] GC before operation: completed in 96.638 ms, heap usage 1.398 GB -> 57.918 MB.
[2024-11-27T22:13:45.136Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:13:48.126Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:13:50.066Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:13:52.081Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:13:53.024Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:13:53.975Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:13:55.912Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:13:56.857Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:13:56.857Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-27T22:13:56.857Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:13:56.857Z] Movies recommended for you:
[2024-11-27T22:13:56.857Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:13:56.857Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:13:56.857Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14257.497 ms) ======
[2024-11-27T22:13:56.857Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-27T22:13:56.857Z] GC before operation: completed in 91.172 ms, heap usage 350.820 MB -> 53.616 MB.
[2024-11-27T22:13:58.800Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:14:00.746Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:14:02.686Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:14:04.623Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:14:05.567Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:14:06.511Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:14:08.449Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:14:10.087Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:14:10.087Z] 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.
[2024-11-27T22:14:10.087Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:14:10.087Z] Movies recommended for you:
[2024-11-27T22:14:10.087Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:14:10.087Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:14:10.087Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (12455.576 ms) ======
[2024-11-27T22:14:10.087Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-27T22:14:10.087Z] GC before operation: completed in 91.272 ms, heap usage 470.598 MB -> 54.101 MB.
[2024-11-27T22:14:12.026Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:14:13.980Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:14:14.932Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:14:17.928Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:14:18.874Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:14:19.819Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:14:21.758Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:14:33.225Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:14:33.225Z] 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.
[2024-11-27T22:14:33.225Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:14:33.225Z] Movies recommended for you:
[2024-11-27T22:14:33.225Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:14:33.225Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:14:33.225Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13084.265 ms) ======
[2024-11-27T22:14:33.225Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-27T22:14:33.225Z] GC before operation: completed in 134.951 ms, heap usage 2.531 GB -> 59.156 MB.
[2024-11-27T22:14:33.227Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:14:33.227Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:14:33.227Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:14:33.227Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:14:33.227Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:14:33.227Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:14:34.172Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:14:35.117Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:14:35.117Z] 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.
[2024-11-27T22:14:35.117Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:14:35.117Z] Movies recommended for you:
[2024-11-27T22:14:35.117Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:14:35.117Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:14:35.117Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (12391.059 ms) ======
[2024-11-27T22:14:35.117Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-27T22:14:35.117Z] GC before operation: completed in 100.950 ms, heap usage 354.583 MB -> 54.096 MB.
[2024-11-27T22:14:37.055Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:14:38.992Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:14:40.929Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:14:42.870Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:14:43.813Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:14:44.758Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:14:45.701Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:14:46.789Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:14:46.789Z] 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.
[2024-11-27T22:14:46.789Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:14:47.732Z] Movies recommended for you:
[2024-11-27T22:14:47.732Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:14:47.732Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:14:47.732Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (11907.712 ms) ======
[2024-11-27T22:14:47.732Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-27T22:14:47.732Z] GC before operation: completed in 96.501 ms, heap usage 457.115 MB -> 54.397 MB.
[2024-11-27T22:14:49.668Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:14:50.618Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:14:52.588Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:14:54.525Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:14:55.470Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:14:56.414Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:14:58.356Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:14:59.301Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:14:59.301Z] 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.
[2024-11-27T22:14:59.301Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:14:59.301Z] Movies recommended for you:
[2024-11-27T22:14:59.301Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:14:59.301Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:14:59.301Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (11911.305 ms) ======
[2024-11-27T22:14:59.301Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-27T22:14:59.301Z] GC before operation: completed in 96.883 ms, heap usage 502.906 MB -> 54.725 MB.
[2024-11-27T22:15:01.236Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:15:03.175Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:15:05.112Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:15:07.051Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:15:07.996Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:15:08.942Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:15:09.887Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:15:11.825Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:15:11.825Z] 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.
[2024-11-27T22:15:11.825Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:15:11.825Z] Movies recommended for you:
[2024-11-27T22:15:11.825Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:15:11.825Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:15:11.825Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12206.297 ms) ======
[2024-11-27T22:15:11.825Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-27T22:15:11.825Z] GC before operation: completed in 98.629 ms, heap usage 770.792 MB -> 57.997 MB.
[2024-11-27T22:15:14.454Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:15:15.398Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:15:17.342Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:15:19.279Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:15:20.226Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:15:21.170Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:15:22.113Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:15:24.052Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:15:24.052Z] 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.
[2024-11-27T22:15:24.052Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:15:24.052Z] Movies recommended for you:
[2024-11-27T22:15:24.052Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:15:24.052Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:15:24.052Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12198.110 ms) ======
[2024-11-27T22:15:24.052Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-27T22:15:24.052Z] GC before operation: completed in 108.224 ms, heap usage 403.271 MB -> 54.496 MB.
[2024-11-27T22:15:25.988Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:15:27.924Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:15:29.862Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:15:31.806Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:15:32.751Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:15:33.694Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:15:34.660Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:15:35.604Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:15:36.553Z] 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.
[2024-11-27T22:15:36.553Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:15:36.553Z] Movies recommended for you:
[2024-11-27T22:15:36.553Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:15:36.553Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:15:36.553Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12258.715 ms) ======
[2024-11-27T22:15:36.553Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-27T22:15:36.553Z] GC before operation: completed in 162.356 ms, heap usage 2.537 GB -> 59.157 MB.
[2024-11-27T22:15:38.555Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:15:40.667Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:15:41.610Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:15:43.547Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:15:44.498Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:15:45.454Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:15:46.406Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:15:48.417Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:15:48.417Z] 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.
[2024-11-27T22:15:48.417Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:15:48.417Z] Movies recommended for you:
[2024-11-27T22:15:48.417Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:15:48.417Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:15:48.417Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (11792.995 ms) ======
[2024-11-27T22:15:48.417Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-27T22:15:48.417Z] GC before operation: completed in 100.563 ms, heap usage 363.123 MB -> 54.428 MB.
[2024-11-27T22:15:50.354Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:15:52.297Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:15:54.234Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:15:56.171Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:15:57.114Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:15:58.061Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:15:59.004Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:15:59.946Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:15:59.946Z] 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.
[2024-11-27T22:15:59.946Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:16:00.888Z] Movies recommended for you:
[2024-11-27T22:16:00.888Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:16:00.888Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:16:00.888Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (12188.026 ms) ======
[2024-11-27T22:16:00.888Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-27T22:16:00.888Z] GC before operation: completed in 93.412 ms, heap usage 457.547 MB -> 54.721 MB.
[2024-11-27T22:16:02.825Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:16:04.852Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:16:06.786Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:16:08.417Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:16:09.364Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:16:10.307Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:16:11.251Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:16:13.186Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:16:13.186Z] 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.
[2024-11-27T22:16:13.186Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:16:13.186Z] Movies recommended for you:
[2024-11-27T22:16:13.186Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:16:13.186Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:16:13.186Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12498.913 ms) ======
[2024-11-27T22:16:13.186Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-27T22:16:13.186Z] GC before operation: completed in 97.592 ms, heap usage 2.308 GB -> 59.329 MB.
[2024-11-27T22:16:15.122Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:16:17.060Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:16:18.997Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:16:20.933Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:16:21.875Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:16:22.819Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:16:23.762Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:16:24.705Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:16:24.705Z] 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.
[2024-11-27T22:16:24.705Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:16:24.705Z] Movies recommended for you:
[2024-11-27T22:16:24.705Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:16:24.705Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:16:24.705Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (11988.974 ms) ======
[2024-11-27T22:16:24.705Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-27T22:16:25.648Z] GC before operation: completed in 92.871 ms, heap usage 359.828 MB -> 54.474 MB.
[2024-11-27T22:16:27.587Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:16:28.529Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:16:30.468Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:16:32.405Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:16:33.361Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:16:35.297Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:16:36.240Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:16:37.183Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:16:37.183Z] 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.
[2024-11-27T22:16:37.183Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:16:37.183Z] Movies recommended for you:
[2024-11-27T22:16:37.183Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:16:37.183Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:16:37.183Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12113.221 ms) ======
[2024-11-27T22:16:37.183Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-27T22:16:37.183Z] GC before operation: completed in 100.485 ms, heap usage 454.557 MB -> 54.690 MB.
[2024-11-27T22:16:39.119Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:16:41.056Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:16:42.994Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:16:44.931Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:16:45.877Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:16:46.986Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:16:47.943Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:16:48.887Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:16:49.834Z] 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.
[2024-11-27T22:16:49.834Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:16:49.834Z] Movies recommended for you:
[2024-11-27T22:16:49.834Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:16:49.834Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:16:49.834Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12089.562 ms) ======
[2024-11-27T22:16:49.834Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-27T22:16:49.834Z] GC before operation: completed in 101.381 ms, heap usage 457.998 MB -> 54.554 MB.
[2024-11-27T22:16:51.772Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:16:53.707Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:16:54.728Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:16:56.666Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:16:57.609Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:16:58.551Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:17:00.503Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:17:01.940Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:17:01.940Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-27T22:17:01.940Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:17:01.940Z] Movies recommended for you:
[2024-11-27T22:17:01.940Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:17:01.940Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:17:01.940Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (11713.553 ms) ======
[2024-11-27T22:17:01.940Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-27T22:17:01.940Z] GC before operation: completed in 93.791 ms, heap usage 456.397 MB -> 54.676 MB.
[2024-11-27T22:17:02.920Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:17:04.858Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:17:06.798Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:17:08.734Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:17:09.679Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:17:10.622Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:17:11.565Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:17:13.542Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:17:13.542Z] 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.
[2024-11-27T22:17:13.543Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:17:13.543Z] Movies recommended for you:
[2024-11-27T22:17:13.543Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:17:13.543Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:17:13.543Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (11826.304 ms) ======
[2024-11-27T22:17:13.543Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-27T22:17:13.543Z] GC before operation: completed in 94.788 ms, heap usage 631.342 MB -> 58.316 MB.
[2024-11-27T22:17:15.486Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-27T22:17:17.426Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-27T22:17:19.361Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-27T22:17:20.304Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-27T22:17:21.248Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-27T22:17:23.184Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-27T22:17:24.128Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-27T22:17:25.070Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-27T22:17:25.070Z] 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.
[2024-11-27T22:17:25.070Z] The best model improves the baseline by 14.43%.
[2024-11-27T22:17:25.070Z] Movies recommended for you:
[2024-11-27T22:17:25.070Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-27T22:17:25.070Z] There is no way to check that no silent failure occurred.
[2024-11-27T22:17:25.070Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (11823.868 ms) ======
[2024-11-27T22:17:27.006Z] -----------------------------------
[2024-11-27T22:17:27.006Z] renaissance-movie-lens_0_PASSED
[2024-11-27T22:17:27.006Z] -----------------------------------
[2024-11-27T22:17:27.006Z]
[2024-11-27T22:17:27.006Z] TEST TEARDOWN:
[2024-11-27T22:17:27.006Z] Nothing to be done for teardown.
[2024-11-27T22:17:27.006Z] renaissance-movie-lens_0 Finish Time: Wed Nov 27 22:17:26 2024 Epoch Time (ms): 1732745846151