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 84580 queued and waiting for resources srun: job 84580 has been allocated resources 2024-10-11 07:43:07.978610: 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. 2024-10-11 07:43:31.875722: 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 2024-10-11 07:44:06.241482: 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 2024-10-11 07:44:06.242075: 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 2024-10-11 07:44:06.242084: 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. 2024-10-11 07:44:58.850697: 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. 2024-10-11 07:45:01.124647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79030 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 2024-10-11 07:45:08.409215: 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: 1.3543 - accuracy: 0.5251 1377/1563 [=========================>....] - ETA: 0s - loss: 1.3535 - accuracy: 0.5254 1412/1563 [==========================>...] - ETA: 0s - loss: 1.3518 - accuracy: 0.5261 1447/1563 [==========================>...] - ETA: 0s - loss: 1.3511 - accuracy: 0.5261 1482/1563 [===========================>..] - ETA: 0s - loss: 1.3511 - accuracy: 0.5259 1517/1563 [============================>.] - ETA: 0s - loss: 1.3509 - accuracy: 0.5259 1552/1563 [============================>.] - ETA: 0s - loss: 1.3516 - accuracy: 0.5256 1563/1563 [==============================] - 2s 1ms/step - loss: 1.3516 - accuracy: 0.5254 Epoch 8/10 1/1563 [..............................] - ETA: 2s - loss: 1.7914 - accuracy: 0.4688 36/1563 [..............................] - ETA: 2s - loss: 1.3291 - accuracy: 0.5434 72/1563 [>.............................] - ETA: 2s - loss: 1.3150 - accuracy: 0.5482 107/1563 [=>............................] - ETA: 2s - loss: 1.3145 - accuracy: 0.5441 142/1563 [=>............................] - 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ETA: 0s - loss: 1.3154 - accuracy: 0.5384 1450/1563 [==========================>...] - ETA: 0s - loss: 1.3157 - accuracy: 0.5381 1485/1563 [===========================>..] - ETA: 0s - loss: 1.3159 - accuracy: 0.5383 1521/1563 [============================>.] - ETA: 0s - loss: 1.3176 - accuracy: 0.5379 1556/1563 [============================>.] - ETA: 0s - loss: 1.3168 - accuracy: 0.5382 1563/1563 [==============================] - 2s 1ms/step - loss: 1.3168 - accuracy: 0.5384 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.7106 - accuracy: 0.4688 36/1563 [..............................] - ETA: 2s - loss: 1.2817 - accuracy: 0.5564 71/1563 [>.............................] - ETA: 2s - loss: 1.2841 - accuracy: 0.5506 106/1563 [=>............................] - ETA: 2s - loss: 1.2864 - accuracy: 0.5448 142/1563 [=>............................] - ETA: 2s - loss: 1.2815 - accuracy: 0.5458 178/1563 [==>...........................] - ETA: 1s - loss: 1.2724 - accuracy: 0.5502 213/1563 [===>..........................] - 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ETA: 0s - loss: 1.2534 - accuracy: 0.5594 1563/1563 [==============================] - 2s 1ms/step - loss: 1.2534 - accuracy: 0.5593 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)= 38.160924673080444 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins14468585489709328303.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