renaissance-movie-lens_0
[2024-11-20T23:28:52.063Z] Running test renaissance-movie-lens_0 ...
[2024-11-20T23:28:52.063Z] ===============================================
[2024-11-20T23:28:52.063Z] renaissance-movie-lens_0 Start Time: Wed Nov 20 17:28:51 2024 Epoch Time (ms): 1732145331501
[2024-11-20T23:28:52.063Z] variation: NoOptions
[2024-11-20T23:28:52.063Z] JVM_OPTIONS:
[2024-11-20T23:28:52.063Z] { \
[2024-11-20T23:28:52.063Z] echo ""; echo "TEST SETUP:"; \
[2024-11-20T23:28:52.063Z] echo "Nothing to be done for setup."; \
[2024-11-20T23:28:52.063Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17321446933419/renaissance-movie-lens_0"; \
[2024-11-20T23:28:52.063Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17321446933419/renaissance-movie-lens_0"; \
[2024-11-20T23:28:52.063Z] echo ""; echo "TESTING:"; \
[2024-11-20T23:28:52.063Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_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_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17321446933419/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-20T23:28:52.063Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17321446933419/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-20T23:28:52.063Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-20T23:28:52.063Z] echo "Nothing to be done for teardown."; \
[2024-11-20T23:28:52.063Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17321446933419/TestTargetResult";
[2024-11-20T23:28:52.063Z]
[2024-11-20T23:28:52.063Z] TEST SETUP:
[2024-11-20T23:28:52.063Z] Nothing to be done for setup.
[2024-11-20T23:28:52.063Z]
[2024-11-20T23:28:52.063Z] TESTING:
[2024-11-20T23:28:54.272Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-20T23:28:56.488Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-20T23:28:59.575Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-20T23:28:59.575Z] Training: 60056, validation: 20285, test: 19854
[2024-11-20T23:28:59.575Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-20T23:28:59.575Z] GC before operation: completed in 51.711 ms, heap usage 161.683 MB -> 37.994 MB.
[2024-11-20T23:29:07.875Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:29:11.917Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:29:14.977Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:29:18.035Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:29:19.450Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:29:20.858Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:29:22.278Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:29:24.483Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:29:24.483Z] 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.
[2024-11-20T23:29:24.483Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:29:24.483Z] Movies recommended for you:
[2024-11-20T23:29:24.483Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:29:24.483Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:29:24.483Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24758.322 ms) ======
[2024-11-20T23:29:24.483Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-20T23:29:24.483Z] GC before operation: completed in 66.877 ms, heap usage 680.496 MB -> 58.631 MB.
[2024-11-20T23:29:28.502Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:29:30.705Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:29:33.755Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:29:35.949Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:29:37.373Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:29:38.784Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:29:41.002Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:29:42.425Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:29:42.425Z] 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.
[2024-11-20T23:29:42.425Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:29:42.425Z] Movies recommended for you:
[2024-11-20T23:29:42.425Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:29:42.425Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:29:42.425Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17962.147 ms) ======
[2024-11-20T23:29:42.425Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-20T23:29:43.103Z] GC before operation: completed in 78.484 ms, heap usage 494.971 MB -> 57.050 MB.
[2024-11-20T23:29:46.161Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:29:49.008Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:29:52.085Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:29:55.137Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:29:57.350Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:29:58.765Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:30:00.166Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:30:01.577Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:30:02.298Z] 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.
[2024-11-20T23:30:02.298Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:30:02.298Z] Movies recommended for you:
[2024-11-20T23:30:02.298Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:30:02.298Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:30:02.298Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (19434.471 ms) ======
[2024-11-20T23:30:02.298Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-20T23:30:02.298Z] GC before operation: completed in 62.497 ms, heap usage 351.218 MB -> 54.391 MB.
[2024-11-20T23:30:05.342Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:30:07.549Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:30:10.621Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:30:12.815Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:30:14.243Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:30:15.651Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:30:17.061Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:30:18.500Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:30:19.182Z] 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.
[2024-11-20T23:30:19.182Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:30:19.182Z] Movies recommended for you:
[2024-11-20T23:30:19.182Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:30:19.182Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:30:19.182Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16765.343 ms) ======
[2024-11-20T23:30:19.182Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-20T23:30:19.182Z] GC before operation: completed in 70.465 ms, heap usage 553.926 MB -> 58.275 MB.
[2024-11-20T23:30:22.249Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:30:24.459Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:30:27.530Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:30:29.723Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:30:31.131Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:30:32.543Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:30:33.989Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:30:35.397Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:30:35.397Z] 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.
[2024-11-20T23:30:35.397Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:30:35.397Z] Movies recommended for you:
[2024-11-20T23:30:35.397Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:30:35.398Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:30:35.398Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16589.069 ms) ======
[2024-11-20T23:30:35.398Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-20T23:30:36.077Z] GC before operation: completed in 69.889 ms, heap usage 625.541 MB -> 57.889 MB.
[2024-11-20T23:30:39.128Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:30:41.405Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:30:44.253Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:30:46.570Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:30:47.987Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:30:49.404Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:30:50.807Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:30:52.213Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:30:52.213Z] 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.
[2024-11-20T23:30:52.213Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:30:52.213Z] Movies recommended for you:
[2024-11-20T23:30:52.213Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:30:52.213Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:30:52.213Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16642.655 ms) ======
[2024-11-20T23:30:52.213Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-20T23:30:52.213Z] GC before operation: completed in 61.353 ms, heap usage 572.145 MB -> 59.805 MB.
[2024-11-20T23:30:55.279Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:30:57.487Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:30:59.688Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:31:01.879Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:31:03.283Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:31:04.718Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:31:06.140Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:31:06.815Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:31:07.497Z] 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.
[2024-11-20T23:31:07.497Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:31:07.497Z] Movies recommended for you:
[2024-11-20T23:31:07.497Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:31:07.497Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:31:07.497Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14927.142 ms) ======
[2024-11-20T23:31:07.497Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-20T23:31:07.497Z] GC before operation: completed in 61.423 ms, heap usage 796.416 MB -> 58.360 MB.
[2024-11-20T23:31:10.574Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:31:12.778Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:31:15.001Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:31:17.232Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:31:18.634Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:31:20.052Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:31:21.480Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:31:22.186Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:31:22.863Z] 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.
[2024-11-20T23:31:22.863Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:31:22.863Z] Movies recommended for you:
[2024-11-20T23:31:22.863Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:31:22.863Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:31:22.863Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15292.312 ms) ======
[2024-11-20T23:31:22.863Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-20T23:31:22.863Z] GC before operation: completed in 60.291 ms, heap usage 698.682 MB -> 57.896 MB.
[2024-11-20T23:31:25.049Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:31:27.267Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:31:29.472Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:31:31.699Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:31:33.116Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:31:34.542Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:31:35.946Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:31:37.979Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:31:37.979Z] 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.
[2024-11-20T23:31:37.979Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:31:37.979Z] Movies recommended for you:
[2024-11-20T23:31:37.979Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:31:37.979Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:31:37.979Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14600.655 ms) ======
[2024-11-20T23:31:37.979Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-20T23:31:37.979Z] GC before operation: completed in 80.499 ms, heap usage 510.096 MB -> 57.478 MB.
[2024-11-20T23:31:40.190Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:31:43.309Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:31:45.512Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:31:47.746Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:31:48.454Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:31:49.873Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:31:51.285Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:31:52.697Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:31:52.697Z] 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.
[2024-11-20T23:31:52.697Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:31:53.392Z] Movies recommended for you:
[2024-11-20T23:31:53.392Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:31:53.392Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:31:53.392Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15591.446 ms) ======
[2024-11-20T23:31:53.392Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-20T23:31:53.392Z] GC before operation: completed in 64.459 ms, heap usage 1.129 GB -> 58.676 MB.
[2024-11-20T23:31:56.477Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:31:58.670Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:32:00.894Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:32:03.094Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:32:04.508Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:32:05.922Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:32:07.342Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:32:08.760Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:32:08.760Z] 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.
[2024-11-20T23:32:08.760Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:32:09.440Z] Movies recommended for you:
[2024-11-20T23:32:09.440Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:32:09.440Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:32:09.440Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16059.657 ms) ======
[2024-11-20T23:32:09.440Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-20T23:32:09.440Z] GC before operation: completed in 68.069 ms, heap usage 1.191 GB -> 60.715 MB.
[2024-11-20T23:32:11.648Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:32:14.726Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:32:16.915Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:32:19.123Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:32:20.536Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:32:21.954Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:32:23.358Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:32:24.760Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:32:24.760Z] 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.
[2024-11-20T23:32:24.760Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:32:24.760Z] Movies recommended for you:
[2024-11-20T23:32:24.760Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:32:24.760Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:32:24.760Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15748.231 ms) ======
[2024-11-20T23:32:24.760Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-20T23:32:25.437Z] GC before operation: completed in 72.874 ms, heap usage 747.546 MB -> 60.131 MB.
[2024-11-20T23:32:27.628Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:32:30.589Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:32:32.823Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:32:35.029Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:32:35.718Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:32:37.127Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:32:38.550Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:32:40.003Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:32:40.725Z] 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.
[2024-11-20T23:32:40.725Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:32:40.725Z] Movies recommended for you:
[2024-11-20T23:32:40.725Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:32:40.725Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:32:40.725Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15460.404 ms) ======
[2024-11-20T23:32:40.725Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-20T23:32:40.725Z] GC before operation: completed in 93.737 ms, heap usage 253.628 MB -> 55.460 MB.
[2024-11-20T23:32:42.928Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:32:46.013Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:32:48.243Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:32:50.464Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:32:51.878Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:32:53.283Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:32:54.706Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:32:55.413Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:32:56.089Z] 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.
[2024-11-20T23:32:56.089Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:32:56.089Z] Movies recommended for you:
[2024-11-20T23:32:56.089Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:32:56.089Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:32:56.089Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15376.318 ms) ======
[2024-11-20T23:32:56.089Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-20T23:32:56.089Z] GC before operation: completed in 76.397 ms, heap usage 515.035 MB -> 58.504 MB.
[2024-11-20T23:32:59.152Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:33:01.350Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:33:04.488Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:33:06.682Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:33:08.093Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:33:08.793Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:33:10.221Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:33:11.641Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:33:11.641Z] 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.
[2024-11-20T23:33:11.641Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:33:12.327Z] Movies recommended for you:
[2024-11-20T23:33:12.327Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:33:12.327Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:33:12.327Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15971.440 ms) ======
[2024-11-20T23:33:12.327Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-20T23:33:12.327Z] GC before operation: completed in 60.065 ms, heap usage 508.291 MB -> 58.983 MB.
[2024-11-20T23:33:14.526Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:33:16.828Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:33:19.047Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:33:21.004Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:33:22.437Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:33:23.860Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:33:25.285Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:33:26.728Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:33:26.728Z] 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.
[2024-11-20T23:33:26.728Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:33:27.411Z] Movies recommended for you:
[2024-11-20T23:33:27.411Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:33:27.411Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:33:27.411Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15018.884 ms) ======
[2024-11-20T23:33:27.411Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-20T23:33:27.411Z] GC before operation: completed in 62.035 ms, heap usage 523.137 MB -> 60.107 MB.
[2024-11-20T23:33:29.607Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:33:31.801Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:33:34.014Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:33:36.210Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:33:37.626Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:33:39.032Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:33:40.443Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:33:41.128Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:33:41.807Z] 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.
[2024-11-20T23:33:41.807Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:33:41.807Z] Movies recommended for you:
[2024-11-20T23:33:41.807Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:33:41.807Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:33:41.807Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14483.516 ms) ======
[2024-11-20T23:33:41.807Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-20T23:33:41.807Z] GC before operation: completed in 77.109 ms, heap usage 661.978 MB -> 57.591 MB.
[2024-11-20T23:33:44.889Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:33:47.093Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:33:49.312Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:33:51.518Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:33:52.931Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:33:54.355Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:33:55.790Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:33:56.478Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:33:57.154Z] 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.
[2024-11-20T23:33:57.154Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:33:57.154Z] Movies recommended for you:
[2024-11-20T23:33:57.154Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:33:57.154Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:33:57.154Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15252.169 ms) ======
[2024-11-20T23:33:57.154Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-20T23:33:57.154Z] GC before operation: completed in 100.532 ms, heap usage 559.180 MB -> 58.602 MB.
[2024-11-20T23:33:59.372Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:34:02.428Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:34:04.624Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:34:06.825Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:34:07.513Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:34:09.613Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:34:10.359Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:34:11.772Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:34:11.772Z] 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.
[2024-11-20T23:34:11.772Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:34:11.772Z] Movies recommended for you:
[2024-11-20T23:34:11.772Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:34:11.772Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:34:11.772Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14820.924 ms) ======
[2024-11-20T23:34:11.772Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-20T23:34:11.772Z] GC before operation: completed in 79.042 ms, heap usage 527.393 MB -> 58.940 MB.
[2024-11-20T23:34:14.883Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-20T23:34:17.089Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-20T23:34:19.390Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-20T23:34:21.611Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-20T23:34:22.309Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-20T23:34:23.722Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-20T23:34:25.140Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-20T23:34:26.567Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-20T23:34:26.567Z] 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.
[2024-11-20T23:34:26.567Z] The best model improves the baseline by 14.43%.
[2024-11-20T23:34:26.567Z] Movies recommended for you:
[2024-11-20T23:34:26.567Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-20T23:34:26.567Z] There is no way to check that no silent failure occurred.
[2024-11-20T23:34:26.567Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14760.615 ms) ======
[2024-11-20T23:34:27.991Z] -----------------------------------
[2024-11-20T23:34:27.991Z] renaissance-movie-lens_0_PASSED
[2024-11-20T23:34:27.991Z] -----------------------------------
[2024-11-20T23:34:27.991Z]
[2024-11-20T23:34:27.991Z] TEST TEARDOWN:
[2024-11-20T23:34:27.991Z] Nothing to be done for teardown.
[2024-11-20T23:34:27.991Z] renaissance-movie-lens_0 Finish Time: Wed Nov 20 17:34:27 2024 Epoch Time (ms): 1732145667644