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 85045 queued and waiting for resources srun: job 85045 has been allocated resources 2024-10-16 07:43:07.564475: 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. 2024-10-16 07:43:31.734177: 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 2024-10-16 07:44:05.938854: 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 2024-10-16 07:44:05.939488: 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 2024-10-16 07:44:05.939498: 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. 2024-10-16 07:45:01.564251: 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. 2024-10-16 07:45:03.863184: 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 2024-10-16 07:45:11.745690: 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: 1.5430 - accuracy: 0.4548 1563/1563 [==============================] - 2s 2ms/step - loss: 1.5429 - accuracy: 0.4547 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.3192 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.4520 - accuracy: 0.4779 68/1563 [>.............................] - ETA: 2s - loss: 1.4835 - accuracy: 0.4724 101/1563 [>.............................] - ETA: 2s - loss: 1.4792 - accuracy: 0.4762 134/1563 [=>............................] - ETA: 2s - loss: 1.4826 - accuracy: 0.4757 168/1563 [==>...........................] - ETA: 2s - loss: 1.4848 - accuracy: 0.4767 201/1563 [==>...........................] - ETA: 2s - loss: 1.4897 - accuracy: 0.4714 234/1563 [===>..........................] - ETA: 2s - loss: 1.4888 - accuracy: 0.4709 268/1563 [====>.........................] - ETA: 1s - loss: 1.4908 - accuracy: 0.4715 301/1563 [====>.........................] - ETA: 1s - loss: 1.4936 - accuracy: 0.4711 335/1563 [=====>........................] - 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ETA: 0s - loss: 1.4836 - accuracy: 0.4764 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4839 - accuracy: 0.4761 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.3980 - accuracy: 0.5938 34/1563 [..............................] - ETA: 2s - loss: 1.4504 - accuracy: 0.4881 68/1563 [>.............................] - ETA: 2s - loss: 1.4535 - accuracy: 0.4844 101/1563 [>.............................] - ETA: 2s - loss: 1.4738 - accuracy: 0.4774 134/1563 [=>............................] - ETA: 2s - loss: 1.4671 - accuracy: 0.4788 167/1563 [==>...........................] - ETA: 2s - loss: 1.4568 - accuracy: 0.4852 201/1563 [==>...........................] - ETA: 2s - loss: 1.4670 - accuracy: 0.4796 235/1563 [===>..........................] - ETA: 2s - loss: 1.4668 - accuracy: 0.4779 269/1563 [====>.........................] - ETA: 1s - loss: 1.4603 - accuracy: 0.4790 302/1563 [====>.........................] - ETA: 1s - loss: 1.4543 - accuracy: 0.4819 335/1563 [=====>........................] - 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2s 1ms/step - loss: 1.4333 - accuracy: 0.4940 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.4643 - accuracy: 0.4688 36/1563 [..............................] - ETA: 2s - loss: 1.3713 - accuracy: 0.5252 71/1563 [>.............................] - ETA: 2s - loss: 1.3938 - accuracy: 0.5070 105/1563 [=>............................] - ETA: 2s - loss: 1.4125 - accuracy: 0.5033 140/1563 [=>............................] - ETA: 2s - loss: 1.4078 - accuracy: 0.5049 175/1563 [==>...........................] - ETA: 2s - loss: 1.4060 - accuracy: 0.5021 210/1563 [===>..........................] - ETA: 1s - loss: 1.3947 - accuracy: 0.5063 244/1563 [===>..........................] - ETA: 1s - loss: 1.3909 - accuracy: 0.5067 279/1563 [====>.........................] - ETA: 1s - loss: 1.3884 - accuracy: 0.5093 313/1563 [=====>........................] - ETA: 1s - loss: 1.3943 - accuracy: 0.5079 347/1563 [=====>........................] - ETA: 1s - loss: 1.3959 - accuracy: 0.5077 381/1563 [======>.......................] - 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2s 2ms/step - loss: 1.2533 - accuracy: 0.5595 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)= 40.21230173110962 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins7992797349796482286.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