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 95579 queued and waiting for resources srun: job 95579 has been allocated resources 2024-10-25 07:43:07.884514: 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. 2024-10-25 07:43:32.890332: 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 2024-10-25 07:44:07.269950: 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 2024-10-25 07:44:07.270596: 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 2024-10-25 07:44:07.270605: 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. 2024-10-25 07:45:01.410366: 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. 2024-10-25 07:45:04.014329: 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 2024-10-25 07:45:11.322293: 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: 1.4362 - accuracy: 0.4941 1449/1563 [==========================>...] - ETA: 0s - loss: 1.4359 - accuracy: 0.4941 1482/1563 [===========================>..] - ETA: 0s - loss: 1.4348 - accuracy: 0.4942 1515/1563 [============================>.] - ETA: 0s - loss: 1.4334 - accuracy: 0.4945 1543/1563 [============================>.] - ETA: 0s - loss: 1.4340 - accuracy: 0.4943 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4335 - accuracy: 0.4944 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.5945 - accuracy: 0.5000 31/1563 [..............................] - ETA: 2s - loss: 1.4348 - accuracy: 0.5071 64/1563 [>.............................] - ETA: 2s - loss: 1.4041 - accuracy: 0.5098 97/1563 [>.............................] - ETA: 2s - loss: 1.4044 - accuracy: 0.5106 130/1563 [=>............................] - ETA: 2s - loss: 1.4036 - accuracy: 0.5082 163/1563 [==>...........................] - ETA: 2s - loss: 1.3879 - accuracy: 0.5107 196/1563 [==>...........................] - 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ETA: 0s - loss: 1.3927 - accuracy: 0.5111 1426/1563 [==========================>...] - ETA: 0s - loss: 1.3927 - accuracy: 0.5108 1460/1563 [===========================>..] - ETA: 0s - loss: 1.3916 - accuracy: 0.5113 1494/1563 [===========================>..] - ETA: 0s - loss: 1.3911 - accuracy: 0.5111 1528/1563 [============================>.] - ETA: 0s - loss: 1.3908 - accuracy: 0.5111 1561/1563 [============================>.] - ETA: 0s - loss: 1.3911 - accuracy: 0.5107 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3913 - accuracy: 0.5107 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.1067 - accuracy: 0.6250 29/1563 [..............................] - ETA: 2s - loss: 1.4082 - accuracy: 0.5129 62/1563 [>.............................] - ETA: 2s - loss: 1.3797 - accuracy: 0.5212 95/1563 [>.............................] - ETA: 2s - loss: 1.3793 - accuracy: 0.5155 128/1563 [=>............................] - ETA: 2s - loss: 1.3741 - accuracy: 0.5154 162/1563 [==>...........................] - 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ETA: 0s - loss: 1.2851 - accuracy: 0.5485 1443/1563 [==========================>...] - ETA: 0s - loss: 1.2848 - accuracy: 0.5483 1476/1563 [===========================>..] - ETA: 0s - loss: 1.2850 - accuracy: 0.5480 1509/1563 [===========================>..] - ETA: 0s - loss: 1.2844 - accuracy: 0.5487 1542/1563 [============================>.] - ETA: 0s - loss: 1.2848 - accuracy: 0.5488 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2841 - accuracy: 0.5492 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.1457 - accuracy: 0.4688 35/1563 [..............................] - ETA: 2s - loss: 1.2278 - accuracy: 0.5634 69/1563 [>.............................] - ETA: 2s - loss: 1.2332 - accuracy: 0.5607 102/1563 [>.............................] - ETA: 2s - loss: 1.2394 - accuracy: 0.5616 136/1563 [=>............................] - ETA: 2s - loss: 1.2457 - accuracy: 0.5574 170/1563 [==>...........................] - ETA: 2s - loss: 1.2539 - accuracy: 0.5572 203/1563 [==>...........................] - 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ETA: 1s - loss: 1.2516 - accuracy: 0.5577 630/1563 [===========>..................] - ETA: 1s - loss: 1.2529 - accuracy: 0.5575 663/1563 [===========>..................] - ETA: 1s - loss: 1.2551 - accuracy: 0.5558 696/1563 [============>.................] - ETA: 1s - loss: 1.2524 - accuracy: 0.5572 729/1563 [============>.................] - ETA: 1s - loss: 1.2524 - accuracy: 0.5579 762/1563 [=============>................] - ETA: 1s - loss: 1.2520 - accuracy: 0.5581 796/1563 [==============>...............] - ETA: 1s - loss: 1.2525 - accuracy: 0.5577 829/1563 [==============>...............] - ETA: 1s - loss: 1.2500 - accuracy: 0.5582 863/1563 [===============>..............] - ETA: 1s - loss: 1.2487 - accuracy: 0.5587 897/1563 [================>.............] - ETA: 1s - loss: 1.2493 - accuracy: 0.5587 930/1563 [================>.............] - ETA: 0s - loss: 1.2501 - accuracy: 0.5585 964/1563 [=================>............] - ETA: 0s - loss: 1.2520 - accuracy: 0.5575 998/1563 [==================>...........] - ETA: 0s - loss: 1.2520 - accuracy: 0.5575 1031/1563 [==================>...........] - ETA: 0s - loss: 1.2525 - accuracy: 0.5580 1064/1563 [===================>..........] - ETA: 0s - loss: 1.2512 - accuracy: 0.5588 1097/1563 [====================>.........] - ETA: 0s - loss: 1.2516 - accuracy: 0.5585 1131/1563 [====================>.........] - ETA: 0s - loss: 1.2500 - accuracy: 0.5594 1165/1563 [=====================>........] - ETA: 0s - loss: 1.2522 - accuracy: 0.5593 1199/1563 [======================>.......] - ETA: 0s - loss: 1.2517 - accuracy: 0.5593 1232/1563 [======================>.......] - ETA: 0s - loss: 1.2513 - accuracy: 0.5594 1265/1563 [=======================>......] - ETA: 0s - loss: 1.2526 - accuracy: 0.5587 1299/1563 [=======================>......] - ETA: 0s - loss: 1.2538 - accuracy: 0.5585 1332/1563 [========================>.....] - ETA: 0s - loss: 1.2531 - accuracy: 0.5586 1366/1563 [=========================>....] - ETA: 0s - loss: 1.2516 - accuracy: 0.5593 1400/1563 [=========================>....] - ETA: 0s - loss: 1.2510 - accuracy: 0.5591 1434/1563 [==========================>...] - ETA: 0s - loss: 1.2502 - accuracy: 0.5593 1468/1563 [===========================>..] - ETA: 0s - loss: 1.2519 - accuracy: 0.5591 1501/1563 [===========================>..] - ETA: 0s - loss: 1.2515 - accuracy: 0.5593 1535/1563 [============================>.] - ETA: 0s - loss: 1.2516 - accuracy: 0.5593 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2516 - accuracy: 0.5594 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)= 40.26387095451355 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins6522378001739374974.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