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 98906 queued and waiting for resources srun: job 98906 has been allocated resources 2024-12-01 07:43:02.904170: 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. 2024-12-01 07:43:03.064856: 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 2024-12-01 07:43:04.039764: 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 2024-12-01 07:43:04.039839: 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 2024-12-01 07:43:04.039845: 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. 2024-12-01 07:43:07.076403: 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. 2024-12-01 07:43:07.459657: 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 2024-12-01 07:43:09.526167: 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: 1.4835 - accuracy: 0.4751 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.3709 - accuracy: 0.5625 34/1563 [..............................] - ETA: 2s - loss: 1.4343 - accuracy: 0.5000 67/1563 [>.............................] - ETA: 2s - loss: 1.4381 - accuracy: 0.4995 100/1563 [>.............................] - ETA: 2s - loss: 1.4509 - accuracy: 0.4953 133/1563 [=>............................] - ETA: 2s - loss: 1.4527 - accuracy: 0.4920 166/1563 [==>...........................] - ETA: 2s - loss: 1.4625 - accuracy: 0.4861 199/1563 [==>...........................] - ETA: 2s - loss: 1.4516 - accuracy: 0.4857 232/1563 [===>..........................] - ETA: 2s - loss: 1.4532 - accuracy: 0.4833 265/1563 [====>.........................] - ETA: 1s - loss: 1.4570 - accuracy: 0.4838 298/1563 [====>.........................] - ETA: 1s - loss: 1.4565 - accuracy: 0.4863 331/1563 [=====>........................] - ETA: 1s - loss: 1.4626 - accuracy: 0.4842 364/1563 [=====>........................] - 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ETA: 0s - loss: 1.4332 - accuracy: 0.4948 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4327 - accuracy: 0.4951 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.1612 - accuracy: 0.5938 34/1563 [..............................] - ETA: 2s - loss: 1.3635 - accuracy: 0.5175 67/1563 [>.............................] - ETA: 2s - loss: 1.3721 - accuracy: 0.5168 100/1563 [>.............................] - ETA: 2s - loss: 1.3700 - accuracy: 0.5191 133/1563 [=>............................] - ETA: 2s - loss: 1.3849 - accuracy: 0.5099 166/1563 [==>...........................] - ETA: 2s - loss: 1.3874 - accuracy: 0.5070 198/1563 [==>...........................] - ETA: 2s - loss: 1.3739 - accuracy: 0.5126 231/1563 [===>..........................] - ETA: 2s - loss: 1.3758 - accuracy: 0.5095 264/1563 [====>.........................] - ETA: 2s - loss: 1.3702 - accuracy: 0.5136 297/1563 [====>.........................] - ETA: 1s - loss: 1.3803 - accuracy: 0.5101 330/1563 [=====>........................] - 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ETA: 0s - loss: 1.3864 - accuracy: 0.5127 1539/1563 [============================>.] - ETA: 0s - loss: 1.3864 - accuracy: 0.5126 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3869 - accuracy: 0.5123 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.4351 - accuracy: 0.5000 34/1563 [..............................] - ETA: 2s - loss: 1.4283 - accuracy: 0.5156 66/1563 [>.............................] - ETA: 2s - loss: 1.3817 - accuracy: 0.5227 99/1563 [>.............................] - ETA: 2s - loss: 1.3714 - accuracy: 0.5259 131/1563 [=>............................] - ETA: 2s - loss: 1.3748 - accuracy: 0.5205 164/1563 [==>...........................] - ETA: 2s - loss: 1.3739 - accuracy: 0.5185 197/1563 [==>...........................] - ETA: 2s - loss: 1.3796 - accuracy: 0.5171 230/1563 [===>..........................] - ETA: 2s - loss: 1.3792 - accuracy: 0.5170 263/1563 [====>.........................] - ETA: 2s - loss: 1.3759 - accuracy: 0.5163 296/1563 [====>.........................] - 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ETA: 0s - loss: 1.2460 - accuracy: 0.5603 1005/1563 [==================>...........] - ETA: 0s - loss: 1.2469 - accuracy: 0.5602 1037/1563 [==================>...........] - ETA: 0s - loss: 1.2466 - accuracy: 0.5602 1070/1563 [===================>..........] - ETA: 0s - loss: 1.2471 - accuracy: 0.5597 1102/1563 [====================>.........] - ETA: 0s - loss: 1.2484 - accuracy: 0.5590 1135/1563 [====================>.........] - ETA: 0s - loss: 1.2494 - accuracy: 0.5586 1168/1563 [=====================>........] - ETA: 0s - loss: 1.2501 - accuracy: 0.5589 1201/1563 [======================>.......] - ETA: 0s - loss: 1.2510 - accuracy: 0.5590 1233/1563 [======================>.......] - ETA: 0s - loss: 1.2510 - accuracy: 0.5593 1266/1563 [=======================>......] - ETA: 0s - loss: 1.2501 - accuracy: 0.5597 1299/1563 [=======================>......] - ETA: 0s - loss: 1.2508 - accuracy: 0.5595 1332/1563 [========================>.....] - ETA: 0s - loss: 1.2518 - accuracy: 0.5591 1364/1563 [=========================>....] - ETA: 0s - loss: 1.2521 - accuracy: 0.5587 1396/1563 [=========================>....] - ETA: 0s - loss: 1.2519 - accuracy: 0.5588 1429/1563 [==========================>...] - ETA: 0s - loss: 1.2524 - accuracy: 0.5591 1462/1563 [===========================>..] - ETA: 0s - loss: 1.2519 - accuracy: 0.5594 1494/1563 [===========================>..] - ETA: 0s - loss: 1.2530 - accuracy: 0.5592 1527/1563 [============================>.] - ETA: 0s - loss: 1.2536 - accuracy: 0.5589 1560/1563 [============================>.] - ETA: 0s - loss: 1.2549 - accuracy: 0.5583 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2550 - accuracy: 0.5584 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)= 26.864478826522827 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins17485078819950232521.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