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 84928 queued and waiting for resources srun: job 84928 has been allocated resources 2024-10-15 07:43:08.307682: 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. 2024-10-15 07:43:34.706772: 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 2024-10-15 07:44:13.667222: 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 2024-10-15 07:44:13.667834: 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 2024-10-15 07:44:13.667841: 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. 2024-10-15 07:45:18.767843: 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. 2024-10-15 07:45:21.095765: 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 2024-10-15 07:45:30.384157: 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: 1.5422 - accuracy: 0.4582 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.2647 - accuracy: 0.5312 34/1563 [..............................] - ETA: 2s - loss: 1.5026 - accuracy: 0.4596 67/1563 [>.............................] - ETA: 2s - loss: 1.4917 - accuracy: 0.4697 100/1563 [>.............................] - ETA: 2s - loss: 1.4804 - accuracy: 0.4744 133/1563 [=>............................] - ETA: 2s - loss: 1.4834 - accuracy: 0.4749 164/1563 [==>...........................] - ETA: 2s - loss: 1.4819 - accuracy: 0.4766 197/1563 [==>...........................] - ETA: 2s - loss: 1.4857 - accuracy: 0.4737 230/1563 [===>..........................] - ETA: 2s - loss: 1.4863 - accuracy: 0.4751 263/1563 [====>.........................] - ETA: 2s - loss: 1.4884 - accuracy: 0.4768 296/1563 [====>.........................] - ETA: 2s - loss: 1.4874 - accuracy: 0.4773 329/1563 [=====>........................] - ETA: 1s - loss: 1.4828 - accuracy: 0.4778 362/1563 [=====>........................] - 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ETA: 0s - loss: 1.4807 - accuracy: 0.4763 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4813 - accuracy: 0.4762 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.3049 - accuracy: 0.5312 34/1563 [..............................] - ETA: 2s - loss: 1.4636 - accuracy: 0.4835 68/1563 [>.............................] - ETA: 2s - loss: 1.4909 - accuracy: 0.4738 101/1563 [>.............................] - ETA: 2s - loss: 1.4792 - accuracy: 0.4790 134/1563 [=>............................] - ETA: 2s - loss: 1.4622 - accuracy: 0.4844 167/1563 [==>...........................] - ETA: 2s - loss: 1.4687 - accuracy: 0.4862 200/1563 [==>...........................] - ETA: 2s - loss: 1.4716 - accuracy: 0.4839 234/1563 [===>..........................] - ETA: 2s - loss: 1.4628 - accuracy: 0.4868 267/1563 [====>.........................] - ETA: 1s - loss: 1.4576 - accuracy: 0.4902 300/1563 [====>.........................] - ETA: 1s - loss: 1.4567 - accuracy: 0.4907 333/1563 [=====>........................] - 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ETA: 0s - loss: 1.4324 - accuracy: 0.4973 1562/1563 [============================>.] - ETA: 0s - loss: 1.4313 - accuracy: 0.4978 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4313 - accuracy: 0.4978 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.5600 - accuracy: 0.4062 34/1563 [..............................] - ETA: 2s - loss: 1.3775 - accuracy: 0.5110 67/1563 [>.............................] - ETA: 2s - loss: 1.3912 - accuracy: 0.5075 101/1563 [>.............................] - ETA: 2s - loss: 1.3914 - accuracy: 0.5059 134/1563 [=>............................] - ETA: 2s - loss: 1.3968 - accuracy: 0.5077 168/1563 [==>...........................] - ETA: 2s - loss: 1.3857 - accuracy: 0.5113 201/1563 [==>...........................] - ETA: 2s - loss: 1.3887 - accuracy: 0.5087 234/1563 [===>..........................] - ETA: 2s - loss: 1.3904 - accuracy: 0.5064 267/1563 [====>.........................] - ETA: 1s - loss: 1.3980 - accuracy: 0.5040 300/1563 [====>.........................] - 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ETA: 0s - loss: 1.3874 - accuracy: 0.5116 1543/1563 [============================>.] - ETA: 0s - loss: 1.3878 - accuracy: 0.5115 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3871 - accuracy: 0.5116 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.5994 - accuracy: 0.5000 35/1563 [..............................] - ETA: 2s - loss: 1.3878 - accuracy: 0.5036 69/1563 [>.............................] - ETA: 2s - loss: 1.3533 - accuracy: 0.5163 103/1563 [>.............................] - ETA: 2s - loss: 1.3587 - accuracy: 0.5225 136/1563 [=>............................] - ETA: 2s - loss: 1.3442 - accuracy: 0.5234 170/1563 [==>...........................] - ETA: 2s - loss: 1.3466 - accuracy: 0.5244 204/1563 [==>...........................] - ETA: 2s - loss: 1.3454 - accuracy: 0.5237 239/1563 [===>..........................] - ETA: 1s - loss: 1.3511 - accuracy: 0.5216 273/1563 [====>.........................] - ETA: 1s - loss: 1.3572 - accuracy: 0.5195 307/1563 [====>.........................] - 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ETA: 0s - loss: 1.2853 - accuracy: 0.5487 1543/1563 [============================>.] - ETA: 0s - loss: 1.2844 - accuracy: 0.5489 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2839 - accuracy: 0.5489 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.4349 - accuracy: 0.4062 35/1563 [..............................] - ETA: 2s - loss: 1.3002 - accuracy: 0.5420 68/1563 [>.............................] - ETA: 2s - loss: 1.2456 - accuracy: 0.5639 101/1563 [>.............................] - ETA: 2s - loss: 1.2471 - accuracy: 0.5653 134/1563 [=>............................] - ETA: 2s - loss: 1.2482 - accuracy: 0.5597 168/1563 [==>...........................] - ETA: 2s - loss: 1.2522 - accuracy: 0.5578 201/1563 [==>...........................] - ETA: 2s - loss: 1.2578 - accuracy: 0.5583 234/1563 [===>..........................] - ETA: 2s - loss: 1.2503 - accuracy: 0.5610 267/1563 [====>.........................] - ETA: 1s - loss: 1.2522 - accuracy: 0.5595 300/1563 [====>.........................] - 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ETA: 1s - loss: 1.2592 - accuracy: 0.5574 731/1563 [=============>................] - ETA: 1s - loss: 1.2589 - accuracy: 0.5572 764/1563 [=============>................] - ETA: 1s - loss: 1.2550 - accuracy: 0.5582 798/1563 [==============>...............] - ETA: 1s - loss: 1.2580 - accuracy: 0.5567 831/1563 [==============>...............] - ETA: 1s - loss: 1.2569 - accuracy: 0.5582 864/1563 [===============>..............] - ETA: 1s - loss: 1.2573 - accuracy: 0.5576 898/1563 [================>.............] - ETA: 1s - loss: 1.2558 - accuracy: 0.5581 931/1563 [================>.............] - ETA: 0s - loss: 1.2538 - accuracy: 0.5586 964/1563 [=================>............] - ETA: 0s - loss: 1.2541 - accuracy: 0.5585 997/1563 [==================>...........] - ETA: 0s - loss: 1.2551 - accuracy: 0.5583 1030/1563 [==================>...........] - ETA: 0s - loss: 1.2553 - accuracy: 0.5586 1063/1563 [===================>..........] - ETA: 0s - loss: 1.2551 - accuracy: 0.5592 1097/1563 [====================>.........] - ETA: 0s - loss: 1.2551 - accuracy: 0.5592 1130/1563 [====================>.........] - ETA: 0s - loss: 1.2546 - accuracy: 0.5594 1163/1563 [=====================>........] - ETA: 0s - loss: 1.2547 - accuracy: 0.5591 1196/1563 [=====================>........] - ETA: 0s - loss: 1.2554 - accuracy: 0.5586 1229/1563 [======================>.......] - ETA: 0s - loss: 1.2546 - accuracy: 0.5588 1262/1563 [=======================>......] - ETA: 0s - loss: 1.2545 - accuracy: 0.5586 1295/1563 [=======================>......] - ETA: 0s - loss: 1.2540 - accuracy: 0.5587 1327/1563 [========================>.....] - ETA: 0s - loss: 1.2552 - accuracy: 0.5584 1360/1563 [=========================>....] - ETA: 0s - loss: 1.2540 - accuracy: 0.5586 1393/1563 [=========================>....] - ETA: 0s - loss: 1.2538 - accuracy: 0.5588 1426/1563 [==========================>...] - ETA: 0s - loss: 1.2539 - accuracy: 0.5592 1459/1563 [===========================>..] - ETA: 0s - loss: 1.2547 - accuracy: 0.5593 1492/1563 [===========================>..] - ETA: 0s - loss: 1.2541 - accuracy: 0.5599 1525/1563 [============================>.] - ETA: 0s - loss: 1.2533 - accuracy: 0.5605 1558/1563 [============================>.] - ETA: 0s - loss: 1.2538 - accuracy: 0.5602 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2536 - accuracy: 0.5602 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)= 41.95786142349243 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins3763653525809707520.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