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 85363 queued and waiting for resources srun: job 85363 has been allocated resources 2024-10-18 07:43:08.489404: 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. 2024-10-18 07:43:33.279657: 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 2024-10-18 07:44:10.162515: 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 2024-10-18 07:44:10.163283: 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 2024-10-18 07:44:10.163292: 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. 2024-10-18 07:45:15.594694: 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. 2024-10-18 07:45:18.159627: 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 2024-10-18 07:45:28.699695: 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: 1.5433 - accuracy: 0.4553 1563/1563 [==============================] - 2s 2ms/step - loss: 1.5427 - accuracy: 0.4558 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.5829 - accuracy: 0.4688 34/1563 [..............................] - ETA: 2s - loss: 1.5145 - accuracy: 0.4651 66/1563 [>.............................] - ETA: 2s - loss: 1.5125 - accuracy: 0.4654 98/1563 [>.............................] - ETA: 2s - loss: 1.5046 - accuracy: 0.4684 130/1563 [=>............................] - ETA: 2s - loss: 1.4983 - accuracy: 0.4728 162/1563 [==>...........................] - ETA: 2s - loss: 1.4947 - accuracy: 0.4715 194/1563 [==>...........................] - ETA: 2s - loss: 1.4985 - accuracy: 0.4716 227/1563 [===>..........................] - ETA: 2s - loss: 1.4971 - accuracy: 0.4763 259/1563 [===>..........................] - ETA: 2s - loss: 1.4983 - accuracy: 0.4749 292/1563 [====>.........................] - ETA: 1s - loss: 1.4966 - accuracy: 0.4753 324/1563 [=====>........................] - 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ETA: 0s - loss: 1.2901 - accuracy: 0.5467 1295/1563 [=======================>......] - ETA: 0s - loss: 1.2890 - accuracy: 0.5469 1327/1563 [========================>.....] - ETA: 0s - loss: 1.2891 - accuracy: 0.5471 1359/1563 [=========================>....] - ETA: 0s - loss: 1.2897 - accuracy: 0.5470 1391/1563 [=========================>....] - ETA: 0s - loss: 1.2885 - accuracy: 0.5476 1422/1563 [==========================>...] - ETA: 0s - loss: 1.2887 - accuracy: 0.5473 1454/1563 [==========================>...] - ETA: 0s - loss: 1.2884 - accuracy: 0.5474 1486/1563 [===========================>..] - ETA: 0s - loss: 1.2872 - accuracy: 0.5478 1518/1563 [============================>.] - ETA: 0s - loss: 1.2870 - accuracy: 0.5481 1550/1563 [============================>.] - ETA: 0s - loss: 1.2862 - accuracy: 0.5485 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2863 - accuracy: 0.5485 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.2145 - accuracy: 0.5000 33/1563 [..............................] - 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ETA: 1s - loss: 1.2596 - accuracy: 0.5606 854/1563 [===============>..............] - ETA: 1s - loss: 1.2580 - accuracy: 0.5610 887/1563 [================>.............] - ETA: 1s - loss: 1.2593 - accuracy: 0.5604 920/1563 [================>.............] - ETA: 0s - loss: 1.2595 - accuracy: 0.5594 953/1563 [=================>............] - ETA: 0s - loss: 1.2610 - accuracy: 0.5586 986/1563 [=================>............] - ETA: 0s - loss: 1.2607 - accuracy: 0.5582 1018/1563 [==================>...........] - ETA: 0s - loss: 1.2597 - accuracy: 0.5584 1051/1563 [===================>..........] - ETA: 0s - loss: 1.2595 - accuracy: 0.5587 1084/1563 [===================>..........] - ETA: 0s - loss: 1.2585 - accuracy: 0.5593 1118/1563 [====================>.........] - ETA: 0s - loss: 1.2574 - accuracy: 0.5596 1151/1563 [=====================>........] - ETA: 0s - loss: 1.2578 - accuracy: 0.5589 1184/1563 [=====================>........] - ETA: 0s - loss: 1.2581 - accuracy: 0.5590 1217/1563 [======================>.......] - ETA: 0s - loss: 1.2596 - accuracy: 0.5581 1250/1563 [======================>.......] - ETA: 0s - loss: 1.2599 - accuracy: 0.5578 1283/1563 [=======================>......] - ETA: 0s - loss: 1.2610 - accuracy: 0.5572 1316/1563 [========================>.....] - ETA: 0s - loss: 1.2605 - accuracy: 0.5570 1349/1563 [========================>.....] - ETA: 0s - loss: 1.2599 - accuracy: 0.5571 1382/1563 [=========================>....] - ETA: 0s - loss: 1.2576 - accuracy: 0.5579 1415/1563 [==========================>...] - ETA: 0s - loss: 1.2578 - accuracy: 0.5581 1448/1563 [==========================>...] - ETA: 0s - loss: 1.2570 - accuracy: 0.5586 1481/1563 [===========================>..] - ETA: 0s - loss: 1.2574 - accuracy: 0.5587 1514/1563 [============================>.] - ETA: 0s - loss: 1.2568 - accuracy: 0.5589 1546/1563 [============================>.] - ETA: 0s - loss: 1.2576 - accuracy: 0.5587 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2580 - 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)= 44.48556423187256 hydro04 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins2728937000657327159.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