renaissance-movie-lens_0
[2024-10-31T07:52:42.773Z] Running test renaissance-movie-lens_0 ...
[2024-10-31T07:52:42.773Z] ===============================================
[2024-10-31T07:52:42.773Z] renaissance-movie-lens_0 Start Time: Thu Oct 31 07:52:42 2024 Epoch Time (ms): 1730361162232
[2024-10-31T07:52:42.773Z] variation: NoOptions
[2024-10-31T07:52:42.773Z] JVM_OPTIONS:
[2024-10-31T07:52:42.773Z] { \
[2024-10-31T07:52:42.773Z] echo ""; echo "TEST SETUP:"; \
[2024-10-31T07:52:42.773Z] echo "Nothing to be done for setup."; \
[2024-10-31T07:52:42.773Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17303581844045/renaissance-movie-lens_0"; \
[2024-10-31T07:52:42.773Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17303581844045/renaissance-movie-lens_0"; \
[2024-10-31T07:52:42.773Z] echo ""; echo "TESTING:"; \
[2024-10-31T07:52:42.773Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-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_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17303581844045/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-31T07:52:42.773Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17303581844045/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-31T07:52:42.773Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-31T07:52:42.773Z] echo "Nothing to be done for teardown."; \
[2024-10-31T07:52:42.773Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17303581844045/TestTargetResult";
[2024-10-31T07:52:42.773Z]
[2024-10-31T07:52:42.773Z] TEST SETUP:
[2024-10-31T07:52:42.773Z] Nothing to be done for setup.
[2024-10-31T07:52:42.773Z]
[2024-10-31T07:52:42.773Z] TESTING:
[2024-10-31T07:52:49.562Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-31T07:52:57.753Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-31T07:53:09.405Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-31T07:53:11.175Z] Training: 60056, validation: 20285, test: 19854
[2024-10-31T07:53:11.175Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-31T07:53:11.175Z] GC before operation: completed in 289.231 ms, heap usage 49.336 MB -> 37.181 MB.
[2024-10-31T07:53:37.337Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:53:50.990Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:54:04.816Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:54:18.522Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:54:22.967Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:54:27.906Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:54:33.482Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:54:39.056Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:54:40.634Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-31T07:54:40.634Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:54:41.391Z] Movies recommended for you:
[2024-10-31T07:54:41.391Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:54:41.391Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:54:41.391Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (89709.411 ms) ======
[2024-10-31T07:54:41.391Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-31T07:54:41.391Z] GC before operation: completed in 382.682 ms, heap usage 250.411 MB -> 49.413 MB.
[2024-10-31T07:54:51.203Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:54:59.591Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:55:07.818Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:55:14.631Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:55:20.182Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:55:24.100Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:55:28.512Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:55:31.911Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:55:32.671Z] 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.
[2024-10-31T07:55:32.671Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:55:33.418Z] Movies recommended for you:
[2024-10-31T07:55:33.418Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:55:33.418Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:55:33.418Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (51623.891 ms) ======
[2024-10-31T07:55:33.418Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-31T07:55:33.418Z] GC before operation: completed in 198.006 ms, heap usage 140.151 MB -> 49.650 MB.
[2024-10-31T07:55:40.245Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:55:47.030Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:55:53.948Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:56:00.743Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:56:04.135Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:56:07.605Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:56:11.247Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:56:15.634Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:56:16.397Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-31T07:56:16.397Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:56:16.397Z] Movies recommended for you:
[2024-10-31T07:56:16.397Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:56:16.397Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:56:16.397Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (43069.687 ms) ======
[2024-10-31T07:56:16.397Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-31T07:56:16.397Z] GC before operation: completed in 269.784 ms, heap usage 131.407 MB -> 49.927 MB.
[2024-10-31T07:56:23.206Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:56:30.027Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:56:36.825Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:56:43.620Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:56:46.984Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:56:50.370Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:56:54.811Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:56:58.192Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:56:58.949Z] 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.
[2024-10-31T07:56:58.949Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:56:58.949Z] Movies recommended for you:
[2024-10-31T07:56:58.949Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:56:58.949Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:56:58.949Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (42463.679 ms) ======
[2024-10-31T07:56:58.949Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-31T07:56:59.710Z] GC before operation: completed in 233.687 ms, heap usage 82.752 MB -> 50.896 MB.
[2024-10-31T07:57:05.522Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:57:12.322Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:57:17.839Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:57:22.230Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:57:26.649Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:57:30.008Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:57:33.392Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:57:36.818Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:57:36.818Z] 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.
[2024-10-31T07:57:36.818Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:57:37.579Z] Movies recommended for you:
[2024-10-31T07:57:37.579Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:57:37.579Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:57:37.579Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (37961.499 ms) ======
[2024-10-31T07:57:37.579Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-31T07:57:37.579Z] GC before operation: completed in 158.817 ms, heap usage 270.680 MB -> 50.668 MB.
[2024-10-31T07:57:43.127Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:57:49.889Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:57:55.387Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:58:00.917Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:58:04.264Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:58:07.652Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:58:11.147Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:58:14.532Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:58:15.291Z] 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.
[2024-10-31T07:58:15.291Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:58:15.291Z] Movies recommended for you:
[2024-10-31T07:58:15.291Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:58:15.291Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:58:15.291Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (37919.935 ms) ======
[2024-10-31T07:58:15.291Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-31T07:58:15.291Z] GC before operation: completed in 196.442 ms, heap usage 258.307 MB -> 50.580 MB.
[2024-10-31T07:58:20.833Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:58:26.335Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:58:31.858Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:58:36.242Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:58:39.604Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:58:42.952Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:58:45.952Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:58:49.352Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:58:50.119Z] 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.
[2024-10-31T07:58:50.119Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:58:50.119Z] Movies recommended for you:
[2024-10-31T07:58:50.119Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:58:50.119Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:58:50.119Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (34622.885 ms) ======
[2024-10-31T07:58:50.119Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-31T07:58:50.119Z] GC before operation: completed in 178.661 ms, heap usage 198.431 MB -> 50.688 MB.
[2024-10-31T07:58:55.636Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:59:01.199Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:59:06.769Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:59:12.302Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:59:14.746Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:59:17.180Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:59:21.550Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:59:23.985Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:59:24.742Z] 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.
[2024-10-31T07:59:24.742Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:59:25.509Z] Movies recommended for you:
[2024-10-31T07:59:25.509Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:59:25.509Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:59:25.509Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (34689.379 ms) ======
[2024-10-31T07:59:25.509Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-31T07:59:25.509Z] GC before operation: completed in 233.688 ms, heap usage 316.298 MB -> 51.098 MB.
[2024-10-31T07:59:31.001Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T07:59:35.954Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T07:59:40.341Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T07:59:45.923Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T07:59:48.354Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T07:59:50.770Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T07:59:54.120Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T07:59:57.480Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T07:59:57.480Z] 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.
[2024-10-31T07:59:57.480Z] The best model improves the baseline by 14.52%.
[2024-10-31T07:59:58.242Z] Movies recommended for you:
[2024-10-31T07:59:58.242Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T07:59:58.242Z] There is no way to check that no silent failure occurred.
[2024-10-31T07:59:58.242Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (32687.776 ms) ======
[2024-10-31T07:59:58.242Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-31T07:59:58.242Z] GC before operation: completed in 170.837 ms, heap usage 302.465 MB -> 50.896 MB.
[2024-10-31T08:00:03.764Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:00:08.206Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:00:13.697Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:00:18.079Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:00:21.421Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:00:23.856Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:00:27.734Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:00:30.159Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:00:30.915Z] 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.
[2024-10-31T08:00:30.915Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:00:30.915Z] Movies recommended for you:
[2024-10-31T08:00:30.915Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:00:30.915Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:00:30.915Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (32731.465 ms) ======
[2024-10-31T08:00:30.915Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-31T08:00:30.915Z] GC before operation: completed in 169.371 ms, heap usage 86.929 MB -> 54.042 MB.
[2024-10-31T08:00:36.443Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:00:40.829Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:00:45.224Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:00:50.749Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:00:53.209Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:00:55.649Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:00:59.034Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:01:01.453Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:01:02.203Z] 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.
[2024-10-31T08:01:02.203Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:01:02.203Z] Movies recommended for you:
[2024-10-31T08:01:02.203Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:01:02.203Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:01:02.203Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (31221.027 ms) ======
[2024-10-31T08:01:02.203Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-31T08:01:02.203Z] GC before operation: completed in 183.290 ms, heap usage 354.779 MB -> 50.802 MB.
[2024-10-31T08:01:07.814Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:01:12.196Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:01:18.262Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:01:22.679Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:01:26.019Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:01:28.442Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:01:31.837Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:01:34.274Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:01:34.274Z] 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.
[2024-10-31T08:01:34.274Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:01:35.030Z] Movies recommended for you:
[2024-10-31T08:01:35.030Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:01:35.030Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:01:35.030Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (32248.278 ms) ======
[2024-10-31T08:01:35.030Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-31T08:01:35.030Z] GC before operation: completed in 136.636 ms, heap usage 68.660 MB -> 54.212 MB.
[2024-10-31T08:01:39.424Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:01:44.947Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:01:50.504Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:01:53.880Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:01:57.342Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:01:59.983Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:02:03.633Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:02:06.852Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:02:06.852Z] 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.
[2024-10-31T08:02:06.852Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:02:06.852Z] Movies recommended for you:
[2024-10-31T08:02:06.852Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:02:06.852Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:02:06.852Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (32163.328 ms) ======
[2024-10-31T08:02:06.852Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-31T08:02:07.690Z] GC before operation: completed in 139.036 ms, heap usage 134.997 MB -> 50.971 MB.
[2024-10-31T08:02:12.438Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:02:17.186Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:02:21.915Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:02:26.653Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:02:29.288Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:02:31.920Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:02:34.579Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:02:38.240Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:02:38.240Z] 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.
[2024-10-31T08:02:38.240Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:02:39.066Z] Movies recommended for you:
[2024-10-31T08:02:39.066Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:02:39.066Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:02:39.066Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (31462.180 ms) ======
[2024-10-31T08:02:39.066Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-31T08:02:39.066Z] GC before operation: completed in 206.141 ms, heap usage 183.079 MB -> 50.765 MB.
[2024-10-31T08:02:43.808Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:02:48.518Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:02:54.446Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:02:58.058Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:03:01.268Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:03:03.921Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:03:06.593Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:03:10.238Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:03:10.238Z] 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.
[2024-10-31T08:03:10.238Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:03:11.203Z] Movies recommended for you:
[2024-10-31T08:03:11.203Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:03:11.203Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:03:11.203Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (31828.881 ms) ======
[2024-10-31T08:03:11.203Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-31T08:03:11.203Z] GC before operation: completed in 177.813 ms, heap usage 100.579 MB -> 50.818 MB.
[2024-10-31T08:03:15.917Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:03:20.643Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:03:26.583Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:03:31.314Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:03:34.973Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:03:36.705Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:03:40.373Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:03:43.014Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:03:43.843Z] 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.
[2024-10-31T08:03:43.843Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:03:43.843Z] Movies recommended for you:
[2024-10-31T08:03:43.843Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:03:43.843Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:03:43.843Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (32980.589 ms) ======
[2024-10-31T08:03:43.843Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-31T08:03:43.843Z] GC before operation: completed in 174.195 ms, heap usage 82.041 MB -> 50.889 MB.
[2024-10-31T08:03:48.595Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:03:53.960Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:03:58.695Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:04:03.457Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:04:06.088Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:04:08.731Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:04:12.341Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:04:14.962Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:04:14.962Z] 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.
[2024-10-31T08:04:14.962Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:04:15.792Z] Movies recommended for you:
[2024-10-31T08:04:15.792Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:04:15.792Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:04:15.792Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (31454.588 ms) ======
[2024-10-31T08:04:15.792Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-31T08:04:15.792Z] GC before operation: completed in 187.297 ms, heap usage 439.830 MB -> 54.231 MB.
[2024-10-31T08:04:20.537Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:04:25.247Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:04:31.166Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:04:34.810Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:04:38.459Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:04:41.669Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:04:44.322Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:04:46.975Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:04:46.975Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-31T08:04:46.975Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:04:46.975Z] Movies recommended for you:
[2024-10-31T08:04:46.975Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:04:46.975Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:04:46.975Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (31564.387 ms) ======
[2024-10-31T08:04:46.975Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-31T08:04:47.806Z] GC before operation: completed in 167.171 ms, heap usage 107.060 MB -> 50.884 MB.
[2024-10-31T08:04:52.550Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:04:57.316Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:05:02.104Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:05:06.897Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:05:10.561Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:05:13.212Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:05:15.883Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:05:19.548Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:05:19.548Z] 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.
[2024-10-31T08:05:19.548Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:05:19.548Z] Movies recommended for you:
[2024-10-31T08:05:19.548Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:05:19.548Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:05:19.548Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (32414.577 ms) ======
[2024-10-31T08:05:19.548Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-31T08:05:20.373Z] GC before operation: completed in 196.810 ms, heap usage 82.512 MB -> 54.607 MB.
[2024-10-31T08:05:25.153Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T08:05:29.933Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T08:05:36.500Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T08:05:41.259Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T08:05:43.932Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T08:05:46.596Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T08:05:49.247Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T08:05:52.921Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T08:05:52.921Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-10-31T08:05:52.921Z] The best model improves the baseline by 14.52%.
[2024-10-31T08:05:53.759Z] Movies recommended for you:
[2024-10-31T08:05:53.759Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T08:05:53.759Z] There is no way to check that no silent failure occurred.
[2024-10-31T08:05:53.759Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (33402.494 ms) ======
[2024-10-31T08:05:54.580Z] -----------------------------------
[2024-10-31T08:05:54.580Z] renaissance-movie-lens_0_PASSED
[2024-10-31T08:05:54.580Z] -----------------------------------
[2024-10-31T08:05:54.580Z]
[2024-10-31T08:05:54.580Z] TEST TEARDOWN:
[2024-10-31T08:05:54.580Z] Nothing to be done for teardown.
[2024-10-31T08:05:54.580Z] renaissance-movie-lens_0 Finish Time: Thu Oct 31 08:05:53 2024 Epoch Time (ms): 1730361953875