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 95398 queued and waiting for resources srun: job 95398 has been allocated resources 2024-10-24 07:43:07.756508: 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. 2024-10-24 07:43:33.149880: 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 2024-10-24 07:44:08.812372: 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 2024-10-24 07:44:08.812972: 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 2024-10-24 07:44:08.812981: 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. 2024-10-24 07:45:03.987693: 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. 2024-10-24 07:45:06.636635: 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 2024-10-24 07:45:15.452725: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. 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ETA: 0s - loss: 1.5422 - accuracy: 0.4553 1563/1563 [==============================] - 2s 2ms/step - loss: 1.5422 - accuracy: 0.4553 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.4637 - accuracy: 0.3750 34/1563 [..............................] - ETA: 2s - loss: 1.4969 - accuracy: 0.4522 67/1563 [>.............................] - ETA: 2s - loss: 1.4849 - accuracy: 0.4664 100/1563 [>.............................] - ETA: 2s - loss: 1.4801 - accuracy: 0.4731 133/1563 [=>............................] - ETA: 2s - loss: 1.4863 - accuracy: 0.4697 166/1563 [==>...........................] - ETA: 2s - loss: 1.4882 - accuracy: 0.4697 199/1563 [==>...........................] - ETA: 2s - loss: 1.4878 - accuracy: 0.4724 233/1563 [===>..........................] - ETA: 2s - loss: 1.4839 - accuracy: 0.4742 266/1563 [====>.........................] - ETA: 1s - loss: 1.4850 - accuracy: 0.4746 298/1563 [====>.........................] - ETA: 1s - loss: 1.4855 - accuracy: 0.4725 331/1563 [=====>........................] - 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ETA: 0s - loss: 1.4830 - accuracy: 0.4764 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4834 - accuracy: 0.4762 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.4345 - accuracy: 0.5625 34/1563 [..............................] - ETA: 2s - loss: 1.4878 - accuracy: 0.4798 68/1563 [>.............................] - ETA: 2s - loss: 1.4831 - accuracy: 0.4738 102/1563 [>.............................] - ETA: 2s - loss: 1.4654 - accuracy: 0.4835 135/1563 [=>............................] - ETA: 2s - loss: 1.4567 - accuracy: 0.4838 169/1563 [==>...........................] - ETA: 2s - loss: 1.4478 - accuracy: 0.4856 203/1563 [==>...........................] - ETA: 2s - loss: 1.4524 - accuracy: 0.4871 237/1563 [===>..........................] - ETA: 2s - loss: 1.4526 - accuracy: 0.4867 270/1563 [====>.........................] - ETA: 1s - loss: 1.4441 - accuracy: 0.4891 303/1563 [====>.........................] - ETA: 1s - loss: 1.4435 - accuracy: 0.4894 336/1563 [=====>........................] - 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ETA: 0s - loss: 1.4318 - accuracy: 0.4951 1563/1563 [==============================] - ETA: 0s - loss: 1.4310 - accuracy: 0.4951 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4310 - accuracy: 0.4951 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.3858 - accuracy: 0.5938 35/1563 [..............................] - ETA: 2s - loss: 1.3703 - accuracy: 0.5304 69/1563 [>.............................] - ETA: 2s - loss: 1.3798 - accuracy: 0.5208 102/1563 [>.............................] - ETA: 2s - loss: 1.3809 - accuracy: 0.5178 134/1563 [=>............................] - ETA: 2s - loss: 1.3958 - accuracy: 0.5107 167/1563 [==>...........................] - ETA: 2s - loss: 1.4046 - accuracy: 0.5051 200/1563 [==>...........................] - ETA: 2s - loss: 1.3989 - accuracy: 0.5089 234/1563 [===>..........................] - ETA: 2s - loss: 1.3972 - accuracy: 0.5083 267/1563 [====>.........................] - ETA: 1s - loss: 1.3971 - accuracy: 0.5075 300/1563 [====>.........................] - 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ETA: 0s - loss: 1.3905 - accuracy: 0.5116 1530/1563 [============================>.] - ETA: 0s - loss: 1.3899 - accuracy: 0.5119 1563/1563 [==============================] - ETA: 0s - loss: 1.3889 - accuracy: 0.5124 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3889 - accuracy: 0.5124 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.3867 - accuracy: 0.4688 34/1563 [..............................] - ETA: 2s - loss: 1.3987 - accuracy: 0.5055 67/1563 [>.............................] - ETA: 2s - loss: 1.3800 - accuracy: 0.5103 100/1563 [>.............................] - ETA: 2s - loss: 1.3800 - accuracy: 0.5122 133/1563 [=>............................] - ETA: 2s - loss: 1.3826 - accuracy: 0.5122 166/1563 [==>...........................] - ETA: 2s - loss: 1.3772 - accuracy: 0.5166 200/1563 [==>...........................] - ETA: 2s - loss: 1.3716 - accuracy: 0.5191 234/1563 [===>..........................] - ETA: 2s - loss: 1.3718 - accuracy: 0.5204 268/1563 [====>.........................] - 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ETA: 0s - loss: 1.2824 - accuracy: 0.5482 1503/1563 [===========================>..] - ETA: 0s - loss: 1.2816 - accuracy: 0.5487 1536/1563 [============================>.] - ETA: 0s - loss: 1.2815 - accuracy: 0.5491 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2819 - accuracy: 0.5489 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.2275 - accuracy: 0.5625 34/1563 [..............................] - ETA: 2s - loss: 1.2494 - accuracy: 0.5469 67/1563 [>.............................] - ETA: 2s - loss: 1.2808 - accuracy: 0.5508 100/1563 [>.............................] - ETA: 2s - loss: 1.2771 - accuracy: 0.5562 132/1563 [=>............................] - ETA: 2s - loss: 1.2726 - accuracy: 0.5585 165/1563 [==>...........................] - ETA: 2s - loss: 1.2547 - accuracy: 0.5638 198/1563 [==>...........................] - ETA: 2s - loss: 1.2522 - accuracy: 0.5641 231/1563 [===>..........................] - ETA: 2s - loss: 1.2552 - accuracy: 0.5621 264/1563 [====>.........................] - 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ETA: 1s - loss: 1.2504 - accuracy: 0.5641 696/1563 [============>.................] - ETA: 1s - loss: 1.2529 - accuracy: 0.5628 730/1563 [=============>................] - ETA: 1s - loss: 1.2523 - accuracy: 0.5642 763/1563 [=============>................] - ETA: 1s - loss: 1.2520 - accuracy: 0.5642 796/1563 [==============>...............] - ETA: 1s - loss: 1.2524 - accuracy: 0.5634 829/1563 [==============>...............] - ETA: 1s - loss: 1.2517 - accuracy: 0.5636 862/1563 [===============>..............] - ETA: 1s - loss: 1.2530 - accuracy: 0.5627 896/1563 [================>.............] - ETA: 1s - loss: 1.2521 - accuracy: 0.5633 930/1563 [================>.............] - ETA: 0s - loss: 1.2506 - accuracy: 0.5632 963/1563 [=================>............] - ETA: 0s - loss: 1.2497 - accuracy: 0.5639 996/1563 [==================>...........] - ETA: 0s - loss: 1.2504 - accuracy: 0.5641 1029/1563 [==================>...........] - ETA: 0s - loss: 1.2493 - accuracy: 0.5640 1062/1563 [===================>..........] - ETA: 0s - loss: 1.2501 - accuracy: 0.5632 1095/1563 [====================>.........] - ETA: 0s - loss: 1.2512 - accuracy: 0.5626 1128/1563 [====================>.........] - ETA: 0s - loss: 1.2500 - accuracy: 0.5632 1162/1563 [=====================>........] - ETA: 0s - loss: 1.2504 - accuracy: 0.5629 1195/1563 [=====================>........] - ETA: 0s - loss: 1.2498 - accuracy: 0.5622 1228/1563 [======================>.......] - ETA: 0s - loss: 1.2496 - accuracy: 0.5627 1262/1563 [=======================>......] - ETA: 0s - loss: 1.2505 - accuracy: 0.5621 1295/1563 [=======================>......] - ETA: 0s - loss: 1.2514 - accuracy: 0.5617 1327/1563 [========================>.....] - ETA: 0s - loss: 1.2504 - accuracy: 0.5616 1360/1563 [=========================>....] - ETA: 0s - loss: 1.2515 - accuracy: 0.5610 1393/1563 [=========================>....] - ETA: 0s - loss: 1.2516 - accuracy: 0.5611 1426/1563 [==========================>...] - ETA: 0s - loss: 1.2525 - accuracy: 0.5608 1459/1563 [===========================>..] - ETA: 0s - loss: 1.2516 - accuracy: 0.5616 1492/1563 [===========================>..] - ETA: 0s - loss: 1.2516 - accuracy: 0.5616 1525/1563 [============================>.] - ETA: 0s - loss: 1.2512 - accuracy: 0.5622 1558/1563 [============================>.] - ETA: 0s - loss: 1.2517 - accuracy: 0.5626 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2519 - accuracy: 0.5625 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)= 41.55332899093628 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins9771090207363109591.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