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 97167 queued and waiting for resources srun: job 97167 has been allocated resources 2024-11-12 07:43:02.568023: 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. 2024-11-12 07:43:02.872219: 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 2024-11-12 07:43:03.929003: 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 2024-11-12 07:43:03.929081: 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 2024-11-12 07:43:03.929087: 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. 2024-11-12 07:43:07.189901: 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. 2024-11-12 07:43:07.543969: 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 2024-11-12 07:43:09.544207: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. 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2s 1ms/step - loss: 1.4829 - accuracy: 0.4775 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.4191 - accuracy: 0.4688 37/1563 [..............................] - ETA: 2s - loss: 1.3895 - accuracy: 0.5017 72/1563 [>.............................] - ETA: 2s - loss: 1.4114 - accuracy: 0.4983 107/1563 [=>............................] - ETA: 2s - loss: 1.4288 - accuracy: 0.4901 142/1563 [=>............................] - ETA: 2s - loss: 1.4321 - accuracy: 0.4912 178/1563 [==>...........................] - ETA: 1s - loss: 1.4362 - accuracy: 0.4903 213/1563 [===>..........................] - ETA: 1s - loss: 1.4310 - accuracy: 0.4941 248/1563 [===>..........................] - ETA: 1s - loss: 1.4327 - accuracy: 0.4955 283/1563 [====>.........................] - ETA: 1s - loss: 1.4256 - accuracy: 0.4970 318/1563 [=====>........................] - ETA: 1s - loss: 1.4294 - accuracy: 0.4964 353/1563 [=====>........................] - ETA: 1s - loss: 1.4280 - accuracy: 0.4964 388/1563 [======>.......................] - 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ETA: 0s - loss: 1.3167 - accuracy: 0.5375 1563/1563 [==============================] - ETA: 0s - loss: 1.3165 - accuracy: 0.5375 1563/1563 [==============================] - 2s 1ms/step - loss: 1.3165 - accuracy: 0.5375 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.2788 - accuracy: 0.5312 37/1563 [..............................] - ETA: 2s - loss: 1.2909 - accuracy: 0.5726 72/1563 [>.............................] - ETA: 2s - loss: 1.2794 - accuracy: 0.5616 108/1563 [=>............................] - ETA: 2s - loss: 1.2692 - accuracy: 0.5593 144/1563 [=>............................] - ETA: 2s - loss: 1.2788 - accuracy: 0.5538 180/1563 [==>...........................] - ETA: 1s - loss: 1.2827 - accuracy: 0.5491 215/1563 [===>..........................] - ETA: 1s - loss: 1.2825 - accuracy: 0.5510 250/1563 [===>..........................] - ETA: 1s - loss: 1.2757 - accuracy: 0.5564 285/1563 [====>.........................] - ETA: 1s - loss: 1.2737 - accuracy: 0.5553 321/1563 [=====>........................] - 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2s 1ms/step - loss: 1.2857 - accuracy: 0.5484 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.2676 - accuracy: 0.5312 37/1563 [..............................] - ETA: 2s - loss: 1.3142 - accuracy: 0.5431 73/1563 [>.............................] - ETA: 2s - loss: 1.2731 - accuracy: 0.5582 109/1563 [=>............................] - ETA: 2s - loss: 1.2759 - accuracy: 0.5522 145/1563 [=>............................] - ETA: 2s - loss: 1.2732 - accuracy: 0.5534 180/1563 [==>...........................] - ETA: 1s - loss: 1.2673 - accuracy: 0.5557 215/1563 [===>..........................] - ETA: 1s - loss: 1.2692 - accuracy: 0.5542 251/1563 [===>..........................] - ETA: 1s - loss: 1.2744 - accuracy: 0.5534 285/1563 [====>.........................] - ETA: 1s - loss: 1.2732 - accuracy: 0.5543 320/1563 [=====>........................] - ETA: 1s - loss: 1.2716 - accuracy: 0.5570 355/1563 [=====>........................] - ETA: 1s - loss: 1.2692 - accuracy: 0.5585 390/1563 [======>.......................] - 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ETA: 0s - loss: 1.2497 - accuracy: 0.5638 1275/1563 [=======================>......] - ETA: 0s - loss: 1.2506 - accuracy: 0.5632 1311/1563 [========================>.....] - ETA: 0s - loss: 1.2509 - accuracy: 0.5628 1347/1563 [========================>.....] - ETA: 0s - loss: 1.2521 - accuracy: 0.5623 1383/1563 [=========================>....] - ETA: 0s - loss: 1.2516 - accuracy: 0.5626 1418/1563 [==========================>...] - ETA: 0s - loss: 1.2520 - accuracy: 0.5624 1453/1563 [==========================>...] - ETA: 0s - loss: 1.2524 - accuracy: 0.5620 1488/1563 [===========================>..] - ETA: 0s - loss: 1.2523 - accuracy: 0.5620 1524/1563 [============================>.] - ETA: 0s - loss: 1.2527 - accuracy: 0.5616 1560/1563 [============================>.] - ETA: 0s - loss: 1.2528 - accuracy: 0.5618 1563/1563 [==============================] - 2s 1ms/step - loss: 1.2529 - accuracy: 0.5617 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)= 24.96775794029236 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins13785848729531285907.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