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 95744 queued and waiting for resources srun: job 95744 has been allocated resources 2024-10-27 07:43:07.781385: 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. 2024-10-27 07:43:32.265059: 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 2024-10-27 07:44:07.154792: 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 2024-10-27 07:44:07.155401: 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 2024-10-27 07:44:07.155409: 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. 2024-10-27 07:45:04.185163: 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. 2024-10-27 07:45:07.153147: 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 2024-10-27 07:45:15.415326: 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: 1.3121 - accuracy: 0.5384 1419/1563 [==========================>...] - ETA: 0s - loss: 1.3116 - accuracy: 0.5390 1453/1563 [==========================>...] - ETA: 0s - loss: 1.3117 - accuracy: 0.5388 1487/1563 [===========================>..] - ETA: 0s - loss: 1.3116 - accuracy: 0.5388 1520/1563 [============================>.] - ETA: 0s - loss: 1.3117 - accuracy: 0.5388 1554/1563 [============================>.] - ETA: 0s - loss: 1.3130 - accuracy: 0.5381 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3131 - accuracy: 0.5381 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.3106 - accuracy: 0.5938 35/1563 [..............................] - ETA: 2s - loss: 1.2469 - accuracy: 0.5679 70/1563 [>.............................] - ETA: 2s - loss: 1.2779 - accuracy: 0.5491 104/1563 [>.............................] - ETA: 2s - loss: 1.2942 - accuracy: 0.5499 138/1563 [=>............................] - ETA: 2s - loss: 1.2998 - accuracy: 0.5478 173/1563 [==>...........................] - 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ETA: 1s - loss: 1.2504 - accuracy: 0.5597 635/1563 [===========>..................] - ETA: 1s - loss: 1.2525 - accuracy: 0.5597 669/1563 [===========>..................] - ETA: 1s - loss: 1.2533 - accuracy: 0.5597 703/1563 [============>.................] - ETA: 1s - loss: 1.2528 - accuracy: 0.5601 737/1563 [=============>................] - ETA: 1s - loss: 1.2552 - accuracy: 0.5592 770/1563 [=============>................] - ETA: 1s - loss: 1.2537 - accuracy: 0.5593 804/1563 [==============>...............] - ETA: 1s - loss: 1.2533 - accuracy: 0.5597 838/1563 [===============>..............] - ETA: 1s - loss: 1.2512 - accuracy: 0.5607 872/1563 [===============>..............] - ETA: 1s - loss: 1.2500 - accuracy: 0.5606 906/1563 [================>.............] - ETA: 0s - loss: 1.2492 - accuracy: 0.5607 940/1563 [=================>............] - ETA: 0s - loss: 1.2497 - accuracy: 0.5607 973/1563 [=================>............] - ETA: 0s - loss: 1.2499 - accuracy: 0.5608 1006/1563 [==================>...........] - ETA: 0s - loss: 1.2499 - accuracy: 0.5613 1040/1563 [==================>...........] - ETA: 0s - loss: 1.2495 - accuracy: 0.5619 1074/1563 [===================>..........] - ETA: 0s - loss: 1.2518 - accuracy: 0.5609 1108/1563 [====================>.........] - ETA: 0s - loss: 1.2521 - accuracy: 0.5611 1142/1563 [====================>.........] - ETA: 0s - loss: 1.2526 - accuracy: 0.5609 1176/1563 [=====================>........] - ETA: 0s - loss: 1.2531 - accuracy: 0.5606 1210/1563 [======================>.......] - ETA: 0s - loss: 1.2539 - accuracy: 0.5608 1243/1563 [======================>.......] - ETA: 0s - loss: 1.2523 - accuracy: 0.5615 1277/1563 [=======================>......] - ETA: 0s - loss: 1.2525 - accuracy: 0.5613 1311/1563 [========================>.....] - ETA: 0s - loss: 1.2522 - accuracy: 0.5617 1345/1563 [========================>.....] - ETA: 0s - loss: 1.2528 - accuracy: 0.5615 1378/1563 [=========================>....] - ETA: 0s - loss: 1.2522 - accuracy: 0.5622 1412/1563 [==========================>...] - ETA: 0s - loss: 1.2516 - accuracy: 0.5620 1446/1563 [==========================>...] - ETA: 0s - loss: 1.2516 - accuracy: 0.5620 1480/1563 [===========================>..] - ETA: 0s - loss: 1.2525 - accuracy: 0.5621 1514/1563 [============================>.] - ETA: 0s - loss: 1.2529 - accuracy: 0.5621 1547/1563 [============================>.] - ETA: 0s - loss: 1.2523 - accuracy: 0.5626 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2516 - accuracy: 0.5627 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)= 41.40657162666321 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins16181743766614178898.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