Started by timer Running as SYSTEM Building in workspace /var/lib/jenkins/jobs/tensorflow/workspace [SSH] script: module load anaconda3_gpu module load cudnn cd python rm -f tensorflow_test.txt srun --nodes=1 --ntasks-per-node=1 --mem=16g \ --time=00:05:00 \ --partition=a100rome \ --account=bbmb-hydro \ --gpus-per-node=1 \ --gpu-bind=closest \ python3 cifar10gpu.py | tee tensorflow_test.txt YVALUE=`grep "wall time seconds" tensorflow_test.txt | cut -d'=' -f2` echo YVALUE=$YVALUE > tensorflow_time.txt [SSH] executing... srun: job 96605 queued and waiting for resources srun: job 96605 has been allocated resources 2024-11-04 07:43:06.011271: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-11-04 07:43:30.281203: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-11-04 07:44:04.801714: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /sw/spack/hydrogpu-2022-06/apps/cudnn/8.4.0.27-11.6-gcc-11.3.0-qbhsfav/lib:/sw/spack/hydrogpu-2022-06/apps/cuda/11.7.0-gcc-11.3.0-3ysno6b/lib64:/sw/spack/hydrogpu-2022-06/apps/openmpi/4.1.4-gcc-11.3.0-i6koipj/lib:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib64:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib 2024-11-04 07:44:04.802463: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /sw/spack/hydrogpu-2022-06/apps/cudnn/8.4.0.27-11.6-gcc-11.3.0-qbhsfav/lib:/sw/spack/hydrogpu-2022-06/apps/cuda/11.7.0-gcc-11.3.0-3ysno6b/lib64:/sw/spack/hydrogpu-2022-06/apps/openmpi/4.1.4-gcc-11.3.0-i6koipj/lib:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib64:/sw/spack/hydrogpu-2022-06/apps/gcc/11.3.0-gcc-8.4.1-kroofxa/lib 2024-11-04 07:44:04.802472: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 2024-11-04 07:45:00.003243: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-11-04 07:45:02.402105: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79148 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe, pci bus id: 0000:21:00.0, compute capability: 8.0 (50000, 32, 32, 3) (10000, 32, 32, 3) Epoch 1/10 2024-11-04 07:45:09.873090: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. 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2s 2ms/step - loss: 1.5392 - accuracy: 0.4581 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.6695 - accuracy: 0.4062 35/1563 [..............................] - ETA: 2s - loss: 1.4581 - accuracy: 0.4866 69/1563 [>.............................] - ETA: 2s - loss: 1.4675 - accuracy: 0.4864 103/1563 [>.............................] - ETA: 2s - loss: 1.4767 - accuracy: 0.4751 136/1563 [=>............................] - ETA: 2s - loss: 1.4708 - accuracy: 0.4782 170/1563 [==>...........................] - ETA: 2s - loss: 1.4759 - accuracy: 0.4761 204/1563 [==>...........................] - ETA: 2s - loss: 1.4773 - accuracy: 0.4761 238/1563 [===>..........................] - ETA: 1s - loss: 1.4732 - accuracy: 0.4793 271/1563 [====>.........................] - ETA: 1s - loss: 1.4714 - accuracy: 0.4799 304/1563 [====>.........................] - ETA: 1s - loss: 1.4692 - accuracy: 0.4809 338/1563 [=====>........................] - ETA: 1s - loss: 1.4655 - accuracy: 0.4827 371/1563 [======>.......................] - 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2s 2ms/step - loss: 1.4783 - accuracy: 0.4813 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.2739 - accuracy: 0.5625 35/1563 [..............................] - ETA: 2s - loss: 1.4233 - accuracy: 0.5009 69/1563 [>.............................] - ETA: 2s - loss: 1.4599 - accuracy: 0.4774 103/1563 [>.............................] - ETA: 2s - loss: 1.4519 - accuracy: 0.4854 136/1563 [=>............................] - ETA: 2s - loss: 1.4445 - accuracy: 0.4881 170/1563 [==>...........................] - ETA: 2s - loss: 1.4331 - accuracy: 0.4934 204/1563 [==>...........................] - ETA: 2s - loss: 1.4317 - accuracy: 0.4945 237/1563 [===>..........................] - ETA: 1s - loss: 1.4358 - accuracy: 0.4917 271/1563 [====>.........................] - ETA: 1s - loss: 1.4385 - accuracy: 0.4903 305/1563 [====>.........................] - ETA: 1s - loss: 1.4411 - accuracy: 0.4908 339/1563 [=====>........................] - ETA: 1s - loss: 1.4437 - accuracy: 0.4893 372/1563 [======>.......................] - 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2s 2ms/step - loss: 1.4318 - accuracy: 0.4957 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.2163 - accuracy: 0.5625 32/1563 [..............................] - ETA: 2s - loss: 1.3618 - accuracy: 0.5146 66/1563 [>.............................] - ETA: 2s - loss: 1.3884 - accuracy: 0.5028 99/1563 [>.............................] - ETA: 2s - loss: 1.3741 - accuracy: 0.5054 132/1563 [=>............................] - ETA: 2s - loss: 1.3717 - accuracy: 0.5130 165/1563 [==>...........................] - ETA: 2s - loss: 1.3789 - accuracy: 0.5108 199/1563 [==>...........................] - ETA: 2s - loss: 1.3829 - accuracy: 0.5083 232/1563 [===>..........................] - ETA: 2s - loss: 1.3824 - accuracy: 0.5121 266/1563 [====>.........................] - ETA: 1s - loss: 1.3803 - accuracy: 0.5129 299/1563 [====>.........................] - ETA: 1s - loss: 1.3825 - accuracy: 0.5131 332/1563 [=====>........................] - ETA: 1s - loss: 1.3829 - accuracy: 0.5127 365/1563 [======>.......................] - 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ETA: 0s - loss: 1.3869 - accuracy: 0.5125 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3874 - accuracy: 0.5124 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.4466 - accuracy: 0.6250 34/1563 [..............................] - ETA: 2s - loss: 1.3508 - accuracy: 0.5460 67/1563 [>.............................] - ETA: 2s - loss: 1.3609 - accuracy: 0.5354 101/1563 [>.............................] - ETA: 2s - loss: 1.3582 - accuracy: 0.5365 134/1563 [=>............................] - ETA: 2s - loss: 1.3603 - accuracy: 0.5352 168/1563 [==>...........................] - ETA: 2s - loss: 1.3586 - accuracy: 0.5337 201/1563 [==>...........................] - ETA: 2s - loss: 1.3602 - accuracy: 0.5317 234/1563 [===>..........................] - ETA: 2s - loss: 1.3546 - accuracy: 0.5322 267/1563 [====>.........................] - ETA: 1s - loss: 1.3592 - accuracy: 0.5307 301/1563 [====>.........................] - ETA: 1s - loss: 1.3626 - accuracy: 0.5282 334/1563 [=====>........................] - 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ETA: 0s - loss: 1.3497 - accuracy: 0.5264 1553/1563 [============================>.] - ETA: 0s - loss: 1.3490 - accuracy: 0.5268 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3488 - accuracy: 0.5268 Epoch 8/10 1/1563 [..............................] - ETA: 2s - loss: 1.2701 - accuracy: 0.4688 33/1563 [..............................] - ETA: 2s - loss: 1.3517 - accuracy: 0.5294 66/1563 [>.............................] - ETA: 2s - loss: 1.3243 - accuracy: 0.5384 99/1563 [>.............................] - ETA: 2s - loss: 1.3305 - accuracy: 0.5341 132/1563 [=>............................] - ETA: 2s - loss: 1.3305 - accuracy: 0.5296 166/1563 [==>...........................] - ETA: 2s - loss: 1.3244 - accuracy: 0.5337 200/1563 [==>...........................] - ETA: 2s - loss: 1.3220 - accuracy: 0.5336 233/1563 [===>..........................] - ETA: 2s - loss: 1.3192 - accuracy: 0.5329 266/1563 [====>.........................] - ETA: 1s - loss: 1.3239 - accuracy: 0.5316 298/1563 [====>.........................] - 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ETA: 0s - loss: 1.2833 - accuracy: 0.5488 1477/1563 [===========================>..] - ETA: 0s - loss: 1.2840 - accuracy: 0.5480 1510/1563 [===========================>..] - ETA: 0s - loss: 1.2843 - accuracy: 0.5481 1543/1563 [============================>.] - ETA: 0s - loss: 1.2840 - accuracy: 0.5482 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2843 - accuracy: 0.5480 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.2534 - accuracy: 0.6250 35/1563 [..............................] - ETA: 2s - loss: 1.2547 - accuracy: 0.5705 69/1563 [>.............................] - ETA: 2s - loss: 1.2605 - accuracy: 0.5611 103/1563 [>.............................] - ETA: 2s - loss: 1.2604 - accuracy: 0.5628 137/1563 [=>............................] - ETA: 2s - loss: 1.2596 - accuracy: 0.5646 171/1563 [==>...........................] - ETA: 2s - loss: 1.2583 - accuracy: 0.5636 204/1563 [==>...........................] - ETA: 2s - loss: 1.2571 - accuracy: 0.5634 238/1563 [===>..........................] - 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ETA: 1s - loss: 1.2584 - accuracy: 0.5598 669/1563 [===========>..................] - ETA: 1s - loss: 1.2601 - accuracy: 0.5595 702/1563 [============>.................] - ETA: 1s - loss: 1.2618 - accuracy: 0.5593 735/1563 [=============>................] - ETA: 1s - loss: 1.2635 - accuracy: 0.5582 767/1563 [=============>................] - ETA: 1s - loss: 1.2631 - accuracy: 0.5575 797/1563 [==============>...............] - ETA: 1s - loss: 1.2628 - accuracy: 0.5577 824/1563 [==============>...............] - ETA: 1s - loss: 1.2623 - accuracy: 0.5576 851/1563 [===============>..............] - ETA: 1s - loss: 1.2585 - accuracy: 0.5585 883/1563 [===============>..............] - ETA: 1s - loss: 1.2601 - accuracy: 0.5575 916/1563 [================>.............] - ETA: 1s - loss: 1.2604 - accuracy: 0.5570 949/1563 [=================>............] - ETA: 0s - loss: 1.2609 - accuracy: 0.5565 982/1563 [=================>............] - ETA: 0s - loss: 1.2600 - accuracy: 0.5564 1015/1563 [==================>...........] - ETA: 0s - loss: 1.2577 - accuracy: 0.5573 1049/1563 [===================>..........] - ETA: 0s - loss: 1.2578 - accuracy: 0.5578 1082/1563 [===================>..........] - ETA: 0s - loss: 1.2554 - accuracy: 0.5580 1115/1563 [====================>.........] - ETA: 0s - loss: 1.2550 - accuracy: 0.5578 1148/1563 [=====================>........] - ETA: 0s - loss: 1.2550 - accuracy: 0.5577 1182/1563 [=====================>........] - ETA: 0s - loss: 1.2538 - accuracy: 0.5580 1215/1563 [======================>.......] - ETA: 0s - loss: 1.2522 - accuracy: 0.5584 1248/1563 [======================>.......] - ETA: 0s - loss: 1.2523 - accuracy: 0.5586 1281/1563 [=======================>......] - ETA: 0s - loss: 1.2519 - accuracy: 0.5589 1314/1563 [========================>.....] - ETA: 0s - loss: 1.2522 - accuracy: 0.5592 1347/1563 [========================>.....] - ETA: 0s - loss: 1.2516 - accuracy: 0.5591 1380/1563 [=========================>....] - ETA: 0s - loss: 1.2529 - accuracy: 0.5586 1413/1563 [==========================>...] - ETA: 0s - loss: 1.2529 - accuracy: 0.5586 1447/1563 [==========================>...] - ETA: 0s - loss: 1.2541 - accuracy: 0.5582 1480/1563 [===========================>..] - ETA: 0s - loss: 1.2535 - accuracy: 0.5589 1515/1563 [============================>.] - ETA: 0s - loss: 1.2534 - accuracy: 0.5593 1549/1563 [============================>.] - ETA: 0s - loss: 1.2537 - accuracy: 0.5594 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2544 - accuracy: 0.5592 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= flatten (Flatten) (None, 3072) 0 dense (Dense) (None, 3000) 9219000 dense_1 (Dense) (None, 1000) 3001000 dense_2 (Dense) (None, 10) 10010 ================================================================= Total params: 12,230,010 Trainable params: 12,230,010 Non-trainable params: 0 _________________________________________________________________ None wall time seconds (without loading test data set)= 40.19088649749756 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins11280433193546265165.sh + scp 'HYDRO_REMOTE:~svchydrojenkins/python/tensorflow_time.txt' /var/lib/jenkins/jobs/tensorflow/workspace Recording plot data Saving plot series data from: /var/lib/jenkins/jobs/tensorflow/workspace/tensorflow_time.txt Finished: SUCCESS