renaissance-movie-lens_0
[2024-11-14T00:06:09.982Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T00:06:09.982Z] ===============================================
[2024-11-14T00:06:09.982Z] renaissance-movie-lens_0 Start Time: Thu Nov 14 00:06:09 2024 Epoch Time (ms): 1731542769510
[2024-11-14T00:06:09.982Z] variation: NoOptions
[2024-11-14T00:06:09.982Z] JVM_OPTIONS:
[2024-11-14T00:06:09.982Z] { \
[2024-11-14T00:06:09.982Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T00:06:09.982Z] echo "Nothing to be done for setup."; \
[2024-11-14T00:06:09.982Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1731541929176/renaissance-movie-lens_0"; \
[2024-11-14T00:06:09.982Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1731541929176/renaissance-movie-lens_0"; \
[2024-11-14T00:06:09.982Z] echo ""; echo "TESTING:"; \
[2024-11-14T00:06:09.982Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1731541929176/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T00:06:09.982Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1731541929176/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T00:06:09.982Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T00:06:09.982Z] echo "Nothing to be done for teardown."; \
[2024-11-14T00:06:09.982Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_1731541929176/TestTargetResult";
[2024-11-14T00:06:09.982Z]
[2024-11-14T00:06:09.982Z] TEST SETUP:
[2024-11-14T00:06:09.982Z] Nothing to be done for setup.
[2024-11-14T00:06:09.982Z]
[2024-11-14T00:06:09.982Z] TESTING:
[2024-11-14T00:06:12.917Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T00:06:15.849Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-14T00:06:21.101Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T00:06:21.101Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T00:06:21.101Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T00:06:21.101Z] GC before operation: completed in 68.429 ms, heap usage 175.938 MB -> 39.469 MB.
[2024-11-14T00:06:29.057Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:06:33.097Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:06:37.137Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:06:40.089Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:06:41.989Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:06:42.914Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:06:45.237Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:06:47.135Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:06:47.135Z] 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.
[2024-11-14T00:06:47.135Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:06:47.135Z] Movies recommended for you:
[2024-11-14T00:06:47.135Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:06:47.135Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:06:47.135Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26029.936 ms) ======
[2024-11-14T00:06:47.135Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T00:06:47.135Z] GC before operation: completed in 143.173 ms, heap usage 873.364 MB -> 56.692 MB.
[2024-11-14T00:06:51.175Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:06:53.075Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:06:56.023Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:06:58.957Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:06:59.884Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:07:01.785Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:07:03.690Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:07:04.616Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:07:04.616Z] 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.
[2024-11-14T00:07:05.541Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:07:05.541Z] Movies recommended for you:
[2024-11-14T00:07:05.541Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:07:05.541Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:07:05.541Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17690.814 ms) ======
[2024-11-14T00:07:05.541Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T00:07:05.541Z] GC before operation: completed in 98.474 ms, heap usage 625.229 MB -> 56.697 MB.
[2024-11-14T00:07:08.477Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:07:10.378Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:07:13.568Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:07:15.467Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:07:16.410Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:07:18.312Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:07:19.244Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:07:21.147Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:07:21.147Z] 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.
[2024-11-14T00:07:21.147Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:07:21.147Z] Movies recommended for you:
[2024-11-14T00:07:21.147Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:07:21.147Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:07:21.147Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16038.663 ms) ======
[2024-11-14T00:07:21.147Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T00:07:21.147Z] GC before operation: completed in 107.435 ms, heap usage 370.656 MB -> 53.702 MB.
[2024-11-14T00:07:24.104Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:07:26.004Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:07:28.942Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:07:30.841Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:07:31.767Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:07:33.671Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:07:34.600Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:07:36.501Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:07:36.501Z] 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.
[2024-11-14T00:07:36.501Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:07:36.501Z] Movies recommended for you:
[2024-11-14T00:07:36.501Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:07:36.501Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:07:36.502Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15185.596 ms) ======
[2024-11-14T00:07:36.502Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T00:07:36.502Z] GC before operation: completed in 100.281 ms, heap usage 308.327 MB -> 53.959 MB.
[2024-11-14T00:07:39.441Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:07:41.344Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:07:43.246Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:07:46.881Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:07:47.806Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:07:48.732Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:07:50.637Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:07:51.564Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:07:51.564Z] 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.
[2024-11-14T00:07:51.564Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:07:51.564Z] Movies recommended for you:
[2024-11-14T00:07:51.564Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:07:51.564Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:07:51.564Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15195.751 ms) ======
[2024-11-14T00:07:51.564Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T00:07:52.492Z] GC before operation: completed in 112.405 ms, heap usage 2.409 GB -> 59.097 MB.
[2024-11-14T00:07:54.395Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:07:57.331Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:07:59.236Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:08:01.141Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:08:03.137Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:08:04.062Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:08:04.993Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:08:06.897Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:08:06.897Z] 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.
[2024-11-14T00:08:06.897Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:08:06.897Z] Movies recommended for you:
[2024-11-14T00:08:06.897Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:08:06.897Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:08:06.897Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15165.558 ms) ======
[2024-11-14T00:08:06.897Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T00:08:06.897Z] GC before operation: completed in 111.156 ms, heap usage 165.192 MB -> 53.948 MB.
[2024-11-14T00:08:09.841Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:08:11.760Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:08:14.710Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:08:16.614Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:08:17.540Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:08:19.449Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:08:20.375Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:08:21.300Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:08:22.226Z] 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.
[2024-11-14T00:08:22.226Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:08:22.226Z] Movies recommended for you:
[2024-11-14T00:08:22.226Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:08:22.226Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:08:22.226Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14726.052 ms) ======
[2024-11-14T00:08:22.226Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T00:08:22.226Z] GC before operation: completed in 105.831 ms, heap usage 348.886 MB -> 54.276 MB.
[2024-11-14T00:08:25.163Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:08:27.063Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:08:28.964Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:08:30.866Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:08:32.769Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:08:33.694Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:08:34.621Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:08:36.523Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:08:36.523Z] 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.
[2024-11-14T00:08:36.523Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:08:36.523Z] Movies recommended for you:
[2024-11-14T00:08:36.523Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:08:36.523Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:08:36.523Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14386.244 ms) ======
[2024-11-14T00:08:36.523Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T00:08:36.523Z] GC before operation: completed in 107.690 ms, heap usage 303.726 MB -> 54.554 MB.
[2024-11-14T00:08:39.460Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:08:41.362Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:08:43.293Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:08:45.195Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:08:46.795Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:08:47.721Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:08:49.620Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:08:50.545Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:08:50.545Z] 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.
[2024-11-14T00:08:50.545Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:08:51.471Z] Movies recommended for you:
[2024-11-14T00:08:51.471Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:08:51.471Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:08:51.471Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14385.680 ms) ======
[2024-11-14T00:08:51.471Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T00:08:51.471Z] GC before operation: completed in 110.906 ms, heap usage 304.422 MB -> 54.355 MB.
[2024-11-14T00:08:53.372Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:08:55.273Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:08:57.183Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:09:00.119Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:09:01.043Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:09:01.969Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:09:03.871Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:09:04.798Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:09:04.798Z] 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.
[2024-11-14T00:09:05.722Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:09:05.722Z] Movies recommended for you:
[2024-11-14T00:09:05.722Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:09:05.722Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:09:05.722Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14215.126 ms) ======
[2024-11-14T00:09:05.722Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T00:09:05.722Z] GC before operation: completed in 112.867 ms, heap usage 2.375 GB -> 59.428 MB.
[2024-11-14T00:09:07.630Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:09:09.529Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:09:12.539Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:09:14.439Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:09:15.363Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:09:17.263Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:09:18.187Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:09:20.086Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:09:20.086Z] 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.
[2024-11-14T00:09:20.086Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:09:20.086Z] Movies recommended for you:
[2024-11-14T00:09:20.086Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:09:20.086Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:09:20.086Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14640.314 ms) ======
[2024-11-14T00:09:20.086Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T00:09:20.086Z] GC before operation: completed in 108.019 ms, heap usage 207.636 MB -> 54.177 MB.
[2024-11-14T00:09:23.023Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:09:24.929Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:09:26.841Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:09:28.747Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:09:30.649Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:09:31.574Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:09:33.474Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:09:34.400Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:09:34.400Z] 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.
[2024-11-14T00:09:34.400Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:09:34.400Z] Movies recommended for you:
[2024-11-14T00:09:34.400Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:09:34.400Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:09:34.400Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14578.948 ms) ======
[2024-11-14T00:09:34.400Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T00:09:35.325Z] GC before operation: completed in 105.523 ms, heap usage 349.109 MB -> 54.545 MB.
[2024-11-14T00:09:37.226Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:09:39.127Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:09:42.067Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:09:43.969Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:09:45.870Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:09:46.796Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:09:47.732Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:09:49.328Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:09:50.253Z] 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.
[2024-11-14T00:09:50.253Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:09:50.253Z] Movies recommended for you:
[2024-11-14T00:09:50.253Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:09:50.253Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:09:50.253Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15106.065 ms) ======
[2024-11-14T00:09:50.253Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T00:09:50.253Z] GC before operation: completed in 109.407 ms, heap usage 251.911 MB -> 54.582 MB.
[2024-11-14T00:09:52.225Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:09:54.124Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:09:57.057Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:09:58.957Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:09:59.882Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:10:01.784Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:10:02.711Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:10:04.624Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:10:04.624Z] 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.
[2024-11-14T00:10:04.624Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:10:04.624Z] Movies recommended for you:
[2024-11-14T00:10:04.624Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:10:04.624Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:10:04.624Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14423.117 ms) ======
[2024-11-14T00:10:04.624Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T00:10:04.624Z] GC before operation: completed in 112.594 ms, heap usage 395.883 MB -> 54.384 MB.
[2024-11-14T00:10:07.566Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:10:09.466Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:10:11.368Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:10:13.336Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:10:15.238Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:10:16.167Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:10:17.092Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:10:18.994Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:10:18.994Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T00:10:18.994Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:10:18.994Z] Movies recommended for you:
[2024-11-14T00:10:18.994Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:10:18.994Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:10:18.994Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14490.515 ms) ======
[2024-11-14T00:10:18.994Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T00:10:18.994Z] GC before operation: completed in 106.919 ms, heap usage 592.714 MB -> 57.929 MB.
[2024-11-14T00:10:21.952Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:10:23.853Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:10:25.755Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:10:27.664Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:10:29.564Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:10:30.491Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:10:32.392Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:10:33.318Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:10:33.318Z] 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.
[2024-11-14T00:10:33.318Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:10:33.318Z] Movies recommended for you:
[2024-11-14T00:10:33.318Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:10:33.318Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:10:33.318Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14340.472 ms) ======
[2024-11-14T00:10:33.318Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T00:10:33.318Z] GC before operation: completed in 109.017 ms, heap usage 307.808 MB -> 54.600 MB.
[2024-11-14T00:10:36.256Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:10:38.158Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:10:40.058Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:10:41.961Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:10:43.863Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:10:44.789Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:10:45.717Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:10:47.619Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:10:47.619Z] 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.
[2024-11-14T00:10:47.619Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:10:47.619Z] Movies recommended for you:
[2024-11-14T00:10:47.619Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:10:47.619Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:10:47.619Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14047.578 ms) ======
[2024-11-14T00:10:47.619Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T00:10:47.619Z] GC before operation: completed in 107.028 ms, heap usage 354.612 MB -> 54.397 MB.
[2024-11-14T00:10:50.559Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:10:52.479Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:10:54.384Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:10:56.287Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:10:58.192Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:10:59.119Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:11:00.047Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:11:01.958Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:11:01.958Z] 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.
[2024-11-14T00:11:01.958Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:11:01.958Z] Movies recommended for you:
[2024-11-14T00:11:01.958Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:11:01.958Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:11:01.958Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14278.199 ms) ======
[2024-11-14T00:11:01.958Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T00:11:01.958Z] GC before operation: completed in 108.910 ms, heap usage 407.837 MB -> 54.590 MB.
[2024-11-14T00:11:04.895Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:11:06.799Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:11:08.701Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:11:10.602Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:11:12.607Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:11:13.533Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:11:15.434Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:11:16.363Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:11:16.363Z] 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.
[2024-11-14T00:11:16.363Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:11:16.363Z] Movies recommended for you:
[2024-11-14T00:11:16.363Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:11:16.363Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:11:16.363Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14297.113 ms) ======
[2024-11-14T00:11:16.363Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T00:11:16.363Z] GC before operation: completed in 111.085 ms, heap usage 208.542 MB -> 54.596 MB.
[2024-11-14T00:11:19.360Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T00:11:21.261Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T00:11:23.161Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T00:11:25.068Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T00:11:26.974Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T00:11:28.602Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T00:11:29.530Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T00:11:30.456Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T00:11:31.382Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T00:11:31.382Z] The best model improves the baseline by 14.43%.
[2024-11-14T00:11:31.382Z] Movies recommended for you:
[2024-11-14T00:11:31.382Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T00:11:31.382Z] There is no way to check that no silent failure occurred.
[2024-11-14T00:11:31.382Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14412.806 ms) ======
[2024-11-14T00:11:32.308Z] -----------------------------------
[2024-11-14T00:11:32.308Z] renaissance-movie-lens_0_PASSED
[2024-11-14T00:11:32.308Z] -----------------------------------
[2024-11-14T00:11:32.308Z]
[2024-11-14T00:11:32.308Z] TEST TEARDOWN:
[2024-11-14T00:11:32.308Z] Nothing to be done for teardown.
[2024-11-14T00:11:32.308Z] renaissance-movie-lens_0 Finish Time: Thu Nov 14 00:11:32 2024 Epoch Time (ms): 1731543092219