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 99138 queued and waiting for resources srun: job 99138 has been allocated resources 2024-12-04 07:43:02.884064: 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. 2024-12-04 07:43:03.156396: 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 2024-12-04 07:43:04.379772: 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 2024-12-04 07:43:04.379853: 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 2024-12-04 07:43:04.379859: 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. 2024-12-04 07:43:08.118673: 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. 2024-12-04 07:43:08.490203: 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 2024-12-04 07:43:10.624436: 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: 1.4842 - accuracy: 0.4768 Epoch 5/10 1/1563 [..............................] - ETA: 1s - loss: 1.5545 - accuracy: 0.4375 40/1563 [..............................] - ETA: 1s - loss: 1.4913 - accuracy: 0.4852 79/1563 [>.............................] - ETA: 1s - loss: 1.4595 - accuracy: 0.4869 118/1563 [=>............................] - ETA: 1s - loss: 1.4605 - accuracy: 0.4886 157/1563 [==>...........................] - ETA: 1s - loss: 1.4608 - accuracy: 0.4865 196/1563 [==>...........................] - ETA: 1s - loss: 1.4452 - accuracy: 0.4903 235/1563 [===>..........................] - ETA: 1s - loss: 1.4509 - accuracy: 0.4875 274/1563 [====>.........................] - ETA: 1s - loss: 1.4520 - accuracy: 0.4877 313/1563 [=====>........................] - ETA: 1s - loss: 1.4525 - accuracy: 0.4901 352/1563 [=====>........................] - ETA: 1s - loss: 1.4506 - accuracy: 0.4903 391/1563 [======>.......................] - ETA: 1s - loss: 1.4482 - accuracy: 0.4915 430/1563 [=======>......................] - 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2s 1ms/step - loss: 1.3909 - accuracy: 0.5113 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.4575 - accuracy: 0.4062 40/1563 [..............................] - ETA: 1s - loss: 1.3506 - accuracy: 0.5188 79/1563 [>.............................] - ETA: 1s - loss: 1.3423 - accuracy: 0.5146 118/1563 [=>............................] - ETA: 1s - loss: 1.3504 - accuracy: 0.5169 157/1563 [==>...........................] - ETA: 1s - loss: 1.3410 - accuracy: 0.5229 196/1563 [==>...........................] - ETA: 1s - loss: 1.3480 - accuracy: 0.5236 235/1563 [===>..........................] - ETA: 1s - loss: 1.3522 - accuracy: 0.5250 274/1563 [====>.........................] - ETA: 1s - loss: 1.3482 - accuracy: 0.5263 313/1563 [=====>........................] - ETA: 1s - loss: 1.3501 - accuracy: 0.5260 352/1563 [=====>........................] - ETA: 1s - loss: 1.3530 - accuracy: 0.5259 391/1563 [======>.......................] - ETA: 1s - loss: 1.3483 - accuracy: 0.5272 430/1563 [=======>......................] - 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2s 1ms/step - loss: 1.3178 - accuracy: 0.5369 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.2628 - accuracy: 0.5000 40/1563 [..............................] - ETA: 1s - loss: 1.2575 - accuracy: 0.5672 79/1563 [>.............................] - ETA: 1s - loss: 1.2633 - accuracy: 0.5554 118/1563 [=>............................] - ETA: 1s - loss: 1.2648 - accuracy: 0.5535 157/1563 [==>...........................] - ETA: 1s - loss: 1.2663 - accuracy: 0.5523 196/1563 [==>...........................] - ETA: 1s - loss: 1.2714 - accuracy: 0.5480 234/1563 [===>..........................] - ETA: 1s - loss: 1.2732 - accuracy: 0.5519 273/1563 [====>.........................] - ETA: 1s - loss: 1.2715 - accuracy: 0.5548 312/1563 [====>.........................] - ETA: 1s - loss: 1.2762 - accuracy: 0.5522 351/1563 [=====>........................] - ETA: 1s - loss: 1.2786 - accuracy: 0.5519 390/1563 [======>.......................] - ETA: 1s - loss: 1.2824 - accuracy: 0.5507 429/1563 [=======>......................] - 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2s 1ms/step - loss: 1.2553 - accuracy: 0.5583 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)= 23.328354597091675 hydro04 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins15254801131465170883.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