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 98395 queued and waiting for resources srun: job 98395 has been allocated resources 2024-11-22 07:43:02.975098: 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. 2024-11-22 07:43:04.048608: 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 2024-11-22 07:43:06.945434: 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 2024-11-22 07:43:06.945840: 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 2024-11-22 07:43:06.945847: 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. 2024-11-22 07:43:13.723920: 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. 2024-11-22 07:43:15.004600: 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 2024-11-22 07:43:17.374992: 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: 1.5422 - accuracy: 0.4556 1563/1563 [==============================] - 2s 2ms/step - loss: 1.5430 - accuracy: 0.4556 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.8302 - accuracy: 0.3750 34/1563 [..............................] - ETA: 2s - loss: 1.5378 - accuracy: 0.4513 67/1563 [>.............................] - ETA: 2s - loss: 1.4997 - accuracy: 0.4734 100/1563 [>.............................] - ETA: 2s - loss: 1.5117 - accuracy: 0.4719 133/1563 [=>............................] - ETA: 2s - loss: 1.5012 - accuracy: 0.4760 166/1563 [==>...........................] - ETA: 2s - loss: 1.4902 - accuracy: 0.4806 199/1563 [==>...........................] - ETA: 2s - loss: 1.4884 - accuracy: 0.4835 234/1563 [===>..........................] - ETA: 2s - loss: 1.4893 - accuracy: 0.4828 268/1563 [====>.........................] - ETA: 1s - loss: 1.4903 - accuracy: 0.4803 301/1563 [====>.........................] - ETA: 1s - loss: 1.4917 - accuracy: 0.4776 334/1563 [=====>........................] - 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ETA: 0s - loss: 1.4825 - accuracy: 0.4755 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4816 - accuracy: 0.4758 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.5991 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.4315 - accuracy: 0.5028 68/1563 [>.............................] - ETA: 2s - loss: 1.3951 - accuracy: 0.5069 102/1563 [>.............................] - ETA: 2s - loss: 1.4129 - accuracy: 0.5012 135/1563 [=>............................] - ETA: 2s - loss: 1.4185 - accuracy: 0.4970 169/1563 [==>...........................] - ETA: 2s - loss: 1.4237 - accuracy: 0.4976 203/1563 [==>...........................] - ETA: 2s - loss: 1.4340 - accuracy: 0.4925 236/1563 [===>..........................] - ETA: 2s - loss: 1.4354 - accuracy: 0.4938 270/1563 [====>.........................] - ETA: 1s - loss: 1.4383 - accuracy: 0.4939 304/1563 [====>.........................] - ETA: 1s - loss: 1.4348 - accuracy: 0.4950 337/1563 [=====>........................] - 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ETA: 0s - loss: 1.4313 - accuracy: 0.4962 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4316 - accuracy: 0.4963 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.5741 - accuracy: 0.3750 35/1563 [..............................] - ETA: 2s - loss: 1.3845 - accuracy: 0.5188 69/1563 [>.............................] - ETA: 2s - loss: 1.3837 - accuracy: 0.5208 103/1563 [>.............................] - ETA: 2s - loss: 1.3905 - accuracy: 0.5115 136/1563 [=>............................] - ETA: 2s - loss: 1.3888 - accuracy: 0.5129 170/1563 [==>...........................] - ETA: 2s - loss: 1.3852 - accuracy: 0.5176 203/1563 [==>...........................] - ETA: 2s - loss: 1.3827 - accuracy: 0.5183 236/1563 [===>..........................] - ETA: 2s - loss: 1.3837 - accuracy: 0.5187 270/1563 [====>.........................] - ETA: 1s - loss: 1.3867 - accuracy: 0.5167 304/1563 [====>.........................] - ETA: 1s - loss: 1.3864 - accuracy: 0.5144 337/1563 [=====>........................] - 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ETA: 0s - loss: 1.3888 - accuracy: 0.5109 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3894 - accuracy: 0.5108 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.6697 - accuracy: 0.4062 34/1563 [..............................] - ETA: 2s - loss: 1.4306 - accuracy: 0.5074 67/1563 [>.............................] - ETA: 2s - loss: 1.3940 - accuracy: 0.5145 100/1563 [>.............................] - ETA: 2s - loss: 1.3691 - accuracy: 0.5175 133/1563 [=>............................] - ETA: 2s - loss: 1.3763 - accuracy: 0.5120 167/1563 [==>...........................] - ETA: 2s - loss: 1.3878 - accuracy: 0.5075 201/1563 [==>...........................] - ETA: 2s - loss: 1.3866 - accuracy: 0.5092 235/1563 [===>..........................] - ETA: 2s - loss: 1.3782 - accuracy: 0.5125 268/1563 [====>.........................] - ETA: 1s - loss: 1.3745 - accuracy: 0.5145 301/1563 [====>.........................] - ETA: 1s - loss: 1.3702 - accuracy: 0.5140 334/1563 [=====>........................] - 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ETA: 0s - loss: 1.3533 - accuracy: 0.5240 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3537 - accuracy: 0.5236 Epoch 8/10 1/1563 [..............................] - ETA: 2s - loss: 1.2526 - accuracy: 0.5000 35/1563 [..............................] - ETA: 2s - loss: 1.3658 - accuracy: 0.5134 69/1563 [>.............................] - ETA: 2s - loss: 1.3674 - accuracy: 0.5168 103/1563 [>.............................] - ETA: 2s - loss: 1.3420 - accuracy: 0.5258 137/1563 [=>............................] - ETA: 2s - loss: 1.3461 - accuracy: 0.5233 171/1563 [==>...........................] - ETA: 2s - loss: 1.3437 - accuracy: 0.5232 204/1563 [==>...........................] - ETA: 2s - loss: 1.3353 - accuracy: 0.5262 237/1563 [===>..........................] - ETA: 1s - loss: 1.3346 - accuracy: 0.5264 271/1563 [====>.........................] - ETA: 1s - loss: 1.3309 - accuracy: 0.5298 305/1563 [====>.........................] - ETA: 1s - loss: 1.3340 - accuracy: 0.5280 339/1563 [=====>........................] - 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ETA: 0s - loss: 1.3159 - accuracy: 0.5368 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3162 - accuracy: 0.5366 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.1151 - accuracy: 0.5938 35/1563 [..............................] - ETA: 2s - loss: 1.2275 - accuracy: 0.5545 69/1563 [>.............................] - ETA: 2s - loss: 1.2655 - accuracy: 0.5485 102/1563 [>.............................] - ETA: 2s - loss: 1.2720 - accuracy: 0.5450 136/1563 [=>............................] - ETA: 2s - loss: 1.2813 - accuracy: 0.5441 170/1563 [==>...........................] - ETA: 2s - loss: 1.2915 - accuracy: 0.5425 203/1563 [==>...........................] - ETA: 2s - loss: 1.2936 - accuracy: 0.5426 236/1563 [===>..........................] - ETA: 2s - loss: 1.2970 - accuracy: 0.5416 270/1563 [====>.........................] - ETA: 1s - loss: 1.2891 - accuracy: 0.5444 304/1563 [====>.........................] - ETA: 1s - loss: 1.2907 - accuracy: 0.5421 336/1563 [=====>........................] - 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ETA: 0s - loss: 1.2822 - accuracy: 0.5480 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2834 - accuracy: 0.5473 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.3511 - accuracy: 0.6250 35/1563 [..............................] - ETA: 2s - loss: 1.2043 - accuracy: 0.5795 70/1563 [>.............................] - ETA: 2s - loss: 1.2021 - accuracy: 0.5871 104/1563 [>.............................] - ETA: 2s - loss: 1.2074 - accuracy: 0.5829 138/1563 [=>............................] - ETA: 2s - loss: 1.2118 - accuracy: 0.5768 172/1563 [==>...........................] - ETA: 2s - loss: 1.2213 - accuracy: 0.5707 206/1563 [==>...........................] - ETA: 2s - loss: 1.2253 - accuracy: 0.5690 240/1563 [===>..........................] - ETA: 1s - loss: 1.2317 - accuracy: 0.5652 273/1563 [====>.........................] - ETA: 1s - loss: 1.2387 - accuracy: 0.5643 307/1563 [====>.........................] - ETA: 1s - loss: 1.2449 - accuracy: 0.5627 340/1563 [=====>........................] - 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ETA: 0s - loss: 1.2529 - accuracy: 0.5586 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2532 - accuracy: 0.5582 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)= 27.813340187072754 hydro04 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins6876956473172606728.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