renaissance-movie-lens_0
[2025-02-07T04:21:57.927Z] Running test renaissance-movie-lens_0 ...
[2025-02-07T04:21:58.237Z] ===============================================
[2025-02-07T04:21:58.237Z] renaissance-movie-lens_0 Start Time: Fri Feb 7 04:21:58 2025 Epoch Time (ms): 1738902118061
[2025-02-07T04:21:58.237Z] variation: NoOptions
[2025-02-07T04:21:58.574Z] JVM_OPTIONS:
[2025-02-07T04:21:58.574Z] { \
[2025-02-07T04:21:58.574Z] echo ""; echo "TEST SETUP:"; \
[2025-02-07T04:21:58.574Z] echo "Nothing to be done for setup."; \
[2025-02-07T04:21:58.574Z] mkdir -p "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17389007255844\\renaissance-movie-lens_0"; \
[2025-02-07T04:21:58.574Z] cd "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17389007255844\\renaissance-movie-lens_0"; \
[2025-02-07T04:21:58.574Z] echo ""; echo "TESTING:"; \
[2025-02-07T04:21:58.574Z] "c:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/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 "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17389007255844\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \
[2025-02-07T04:21:58.574Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/; rm -f -r "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17389007255844\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-07T04:21:58.574Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-07T04:21:58.574Z] echo "Nothing to be done for teardown."; \
[2025-02-07T04:21:58.574Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17389007255844\\TestTargetResult";
[2025-02-07T04:21:58.574Z]
[2025-02-07T04:21:58.574Z] TEST SETUP:
[2025-02-07T04:21:58.574Z] Nothing to be done for setup.
[2025-02-07T04:21:58.574Z]
[2025-02-07T04:21:58.574Z] TESTING:
[2025-02-07T04:22:11.480Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-07T04:22:13.101Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-07T04:22:16.999Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-07T04:22:16.999Z] Training: 60056, validation: 20285, test: 19854
[2025-02-07T04:22:16.999Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-07T04:22:16.999Z] GC before operation: completed in 69.381 ms, heap usage 47.781 MB -> 36.914 MB.
[2025-02-07T04:22:30.097Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:22:40.956Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:22:49.681Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:22:56.802Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:23:02.557Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:23:06.283Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:23:12.045Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:23:16.685Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:23:16.685Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:23:16.685Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:23:17.014Z] Movies recommended for you:
[2025-02-07T04:23:17.014Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:23:17.014Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:23:17.014Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (60024.380 ms) ======
[2025-02-07T04:23:17.014Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-07T04:23:17.014Z] GC before operation: completed in 108.802 ms, heap usage 119.313 MB -> 50.919 MB.
[2025-02-07T04:23:25.734Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:23:34.506Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:23:43.232Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:23:50.358Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:23:55.034Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:23:59.659Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:24:05.430Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:24:09.082Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:24:09.433Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:24:09.433Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:24:09.837Z] Movies recommended for you:
[2025-02-07T04:24:09.837Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:24:09.837Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:24:09.837Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (52593.565 ms) ======
[2025-02-07T04:24:09.837Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-07T04:24:09.837Z] GC before operation: completed in 106.893 ms, heap usage 80.703 MB -> 52.682 MB.
[2025-02-07T04:24:18.562Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:24:27.285Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:24:34.443Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:24:43.140Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:24:46.811Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:24:51.455Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:24:56.081Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:24:59.770Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:25:00.101Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:25:00.101Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:25:00.445Z] Movies recommended for you:
[2025-02-07T04:25:00.445Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:25:00.445Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:25:00.445Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (50509.154 ms) ======
[2025-02-07T04:25:00.445Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-07T04:25:00.445Z] GC before operation: completed in 104.590 ms, heap usage 286.820 MB -> 53.239 MB.
[2025-02-07T04:25:09.160Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:25:16.265Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:25:25.008Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:25:33.705Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:25:37.412Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:25:41.103Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:25:45.739Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:25:50.378Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:25:50.378Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:25:50.378Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:25:50.719Z] Movies recommended for you:
[2025-02-07T04:25:50.719Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:25:50.719Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:25:50.719Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (50206.942 ms) ======
[2025-02-07T04:25:50.719Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-07T04:25:50.719Z] GC before operation: completed in 107.159 ms, heap usage 193.859 MB -> 53.360 MB.
[2025-02-07T04:25:59.449Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:26:06.595Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:26:15.297Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:26:22.397Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:26:27.004Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:26:30.660Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:26:35.302Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:26:39.936Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:26:39.936Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:26:39.936Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:26:39.936Z] Movies recommended for you:
[2025-02-07T04:26:39.936Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:26:39.936Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:26:39.936Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (49259.799 ms) ======
[2025-02-07T04:26:39.936Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-07T04:26:39.936Z] GC before operation: completed in 100.597 ms, heap usage 239.195 MB -> 53.599 MB.
[2025-02-07T04:26:48.701Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:26:55.843Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:27:04.540Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:27:11.673Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:27:16.281Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:27:19.955Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:27:24.586Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:27:29.213Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:27:29.213Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:27:29.213Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:27:29.213Z] Movies recommended for you:
[2025-02-07T04:27:29.213Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:27:29.213Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:27:29.213Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (49133.026 ms) ======
[2025-02-07T04:27:29.213Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-07T04:27:29.213Z] GC before operation: completed in 107.705 ms, heap usage 135.438 MB -> 53.404 MB.
[2025-02-07T04:27:37.935Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:27:45.033Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:27:53.772Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:28:00.883Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:28:05.538Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:28:10.165Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:28:14.813Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:28:18.488Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:28:19.183Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:28:19.183Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:28:19.183Z] Movies recommended for you:
[2025-02-07T04:28:19.183Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:28:19.183Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:28:19.183Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (49882.629 ms) ======
[2025-02-07T04:28:19.183Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-07T04:28:19.183Z] GC before operation: completed in 94.286 ms, heap usage 96.352 MB -> 50.366 MB.
[2025-02-07T04:28:27.913Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:28:35.028Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:28:43.716Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:28:50.862Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:28:55.476Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:29:00.106Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:29:04.743Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:29:09.360Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:29:09.360Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:29:09.691Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:29:09.691Z] Movies recommended for you:
[2025-02-07T04:29:09.691Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:29:09.691Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:29:09.691Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (50406.293 ms) ======
[2025-02-07T04:29:09.691Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-07T04:29:09.691Z] GC before operation: completed in 95.372 ms, heap usage 99.119 MB -> 50.630 MB.
[2025-02-07T04:29:18.421Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:29:25.541Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:29:34.282Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:29:41.389Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:29:45.084Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:29:49.751Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:29:53.474Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:29:58.089Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:29:58.089Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:29:58.089Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:29:58.446Z] Movies recommended for you:
[2025-02-07T04:29:58.446Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:29:58.446Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:29:58.446Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (48567.610 ms) ======
[2025-02-07T04:29:58.446Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-07T04:29:58.446Z] GC before operation: completed in 100.764 ms, heap usage 229.907 MB -> 53.801 MB.
[2025-02-07T04:30:07.174Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:30:15.874Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:30:24.608Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:30:31.704Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:30:35.381Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:30:40.024Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:30:44.682Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:30:49.345Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:30:49.345Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:30:49.345Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:30:49.345Z] Movies recommended for you:
[2025-02-07T04:30:49.345Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:30:49.345Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:30:49.345Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (50931.998 ms) ======
[2025-02-07T04:30:49.345Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-07T04:30:49.677Z] GC before operation: completed in 102.731 ms, heap usage 190.586 MB -> 52.841 MB.
[2025-02-07T04:30:58.379Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:31:05.483Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:31:12.638Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:31:21.347Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:31:25.022Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:31:29.637Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:31:34.295Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:31:37.978Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:31:38.303Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:31:38.303Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:31:38.631Z] Movies recommended for you:
[2025-02-07T04:31:38.631Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:31:38.631Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:31:38.631Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (49023.754 ms) ======
[2025-02-07T04:31:38.631Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-07T04:31:38.631Z] GC before operation: completed in 103.113 ms, heap usage 230.816 MB -> 53.622 MB.
[2025-02-07T04:31:47.369Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:31:54.513Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:32:03.236Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:32:10.313Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:32:14.937Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:32:19.552Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:32:24.179Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:32:27.853Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:32:28.175Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:32:28.176Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:32:28.503Z] Movies recommended for you:
[2025-02-07T04:32:28.503Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:32:28.503Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:32:28.503Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (49796.890 ms) ======
[2025-02-07T04:32:28.503Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-07T04:32:28.503Z] GC before operation: completed in 96.444 ms, heap usage 97.427 MB -> 50.482 MB.
[2025-02-07T04:32:37.235Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:32:44.341Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:32:53.078Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:33:00.203Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:33:04.853Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:33:09.471Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:33:14.128Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:33:18.784Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:33:18.784Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:33:18.784Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:33:19.126Z] Movies recommended for you:
[2025-02-07T04:33:19.126Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:33:19.126Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:33:19.126Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (50448.822 ms) ======
[2025-02-07T04:33:19.126Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-07T04:33:19.126Z] GC before operation: completed in 105.059 ms, heap usage 93.959 MB -> 50.650 MB.
[2025-02-07T04:33:27.933Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:33:36.649Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:33:43.755Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:33:52.512Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:33:56.218Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:34:00.850Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:34:05.488Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:34:10.138Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:34:10.138Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:34:10.138Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:34:10.138Z] Movies recommended for you:
[2025-02-07T04:34:10.138Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:34:10.138Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:34:10.138Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (51065.155 ms) ======
[2025-02-07T04:34:10.138Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-07T04:34:10.138Z] GC before operation: completed in 100.156 ms, heap usage 253.585 MB -> 50.548 MB.
[2025-02-07T04:34:18.848Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:34:26.054Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:34:34.788Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:34:41.900Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:34:46.529Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:34:51.162Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:34:55.788Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:34:59.487Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:34:59.835Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:34:59.835Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:35:00.176Z] Movies recommended for you:
[2025-02-07T04:35:00.176Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:35:00.176Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:35:00.176Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (49764.739 ms) ======
[2025-02-07T04:35:00.176Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-07T04:35:00.176Z] GC before operation: completed in 103.580 ms, heap usage 102.712 MB -> 53.811 MB.
[2025-02-07T04:35:08.910Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:35:16.032Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:35:24.783Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:35:31.908Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:35:36.540Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:35:41.171Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:35:46.926Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:35:50.624Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:35:50.947Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:35:50.947Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:35:50.947Z] Movies recommended for you:
[2025-02-07T04:35:50.947Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:35:50.947Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:35:50.947Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (50886.981 ms) ======
[2025-02-07T04:35:50.947Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-07T04:35:51.270Z] GC before operation: completed in 109.145 ms, heap usage 106.869 MB -> 50.637 MB.
[2025-02-07T04:35:59.999Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:36:07.144Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:36:15.867Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:36:22.989Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:36:27.631Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:36:31.300Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:36:37.110Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:36:40.786Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:36:41.115Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:36:41.115Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:36:41.115Z] Movies recommended for you:
[2025-02-07T04:36:41.115Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:36:41.115Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:36:41.115Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (50035.192 ms) ======
[2025-02-07T04:36:41.115Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-07T04:36:41.441Z] GC before operation: completed in 104.808 ms, heap usage 189.216 MB -> 50.566 MB.
[2025-02-07T04:36:50.189Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:36:57.284Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:37:06.005Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:37:13.098Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:37:16.753Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:37:21.400Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:37:26.005Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:37:30.657Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:37:30.657Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:37:30.657Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:37:30.657Z] Movies recommended for you:
[2025-02-07T04:37:30.657Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:37:30.657Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:37:30.657Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (49478.744 ms) ======
[2025-02-07T04:37:30.657Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-07T04:37:30.997Z] GC before operation: completed in 101.095 ms, heap usage 149.046 MB -> 50.626 MB.
[2025-02-07T04:37:39.734Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:37:48.433Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:37:55.569Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:38:04.303Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:38:08.011Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:38:12.662Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:38:17.303Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:38:22.012Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:38:22.012Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:38:22.012Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:38:22.338Z] Movies recommended for you:
[2025-02-07T04:38:22.338Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:38:22.338Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:38:22.338Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (51264.078 ms) ======
[2025-02-07T04:38:22.338Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-07T04:38:22.338Z] GC before operation: completed in 103.421 ms, heap usage 115.513 MB -> 50.731 MB.
[2025-02-07T04:38:31.031Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-07T04:38:38.152Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-07T04:38:45.283Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-07T04:38:52.431Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-07T04:38:57.093Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-07T04:39:01.728Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-07T04:39:06.375Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-07T04:39:10.994Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-07T04:39:10.994Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-07T04:39:10.994Z] The best model improves the baseline by 14.52%.
[2025-02-07T04:39:10.994Z] Movies recommended for you:
[2025-02-07T04:39:10.994Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-07T04:39:10.994Z] There is no way to check that no silent failure occurred.
[2025-02-07T04:39:10.994Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (48795.374 ms) ======
[2025-02-07T04:39:11.686Z] -----------------------------------
[2025-02-07T04:39:11.686Z] renaissance-movie-lens_0_PASSED
[2025-02-07T04:39:11.686Z] -----------------------------------
[2025-02-07T04:39:11.998Z]
[2025-02-07T04:39:11.998Z] TEST TEARDOWN:
[2025-02-07T04:39:11.998Z] Nothing to be done for teardown.
[2025-02-07T04:39:12.324Z] renaissance-movie-lens_0 Finish Time: Fri Feb 7 04:39:12 2025 Epoch Time (ms): 1738903152033