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 97274 queued and waiting for resources srun: job 97274 has been allocated resources 2024-11-13 07:43:02.661746: 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. 2024-11-13 07:43:03.817929: 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 2024-11-13 07:43:06.960855: 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 2024-11-13 07:43:06.962174: 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 2024-11-13 07:43:06.962183: 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. 2024-11-13 07:43:14.292292: 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. 2024-11-13 07:43:15.613885: 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 2024-11-13 07:43:19.822957: 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: 0.4558 1563/1563 [==============================] - 2s 2ms/step - loss: 1.5427 - accuracy: 0.4561 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.5032 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.4828 - accuracy: 0.4632 67/1563 [>.............................] - ETA: 2s - loss: 1.4994 - accuracy: 0.4622 100/1563 [>.............................] - ETA: 2s - loss: 1.4927 - accuracy: 0.4678 133/1563 [=>............................] - ETA: 2s - loss: 1.5061 - accuracy: 0.4662 167/1563 [==>...........................] - ETA: 2s - loss: 1.5108 - accuracy: 0.4695 201/1563 [==>...........................] - ETA: 2s - loss: 1.5069 - accuracy: 0.4712 234/1563 [===>..........................] - ETA: 2s - loss: 1.5150 - accuracy: 0.4663 267/1563 [====>.........................] - ETA: 1s - loss: 1.5045 - accuracy: 0.4716 300/1563 [====>.........................] - ETA: 1s - loss: 1.5033 - accuracy: 0.4726 333/1563 [=====>........................] - 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ETA: 0s - loss: 1.2832 - accuracy: 0.5498 1488/1563 [===========================>..] - ETA: 0s - loss: 1.2837 - accuracy: 0.5495 1521/1563 [============================>.] - ETA: 0s - loss: 1.2836 - accuracy: 0.5496 1554/1563 [============================>.] - ETA: 0s - loss: 1.2832 - accuracy: 0.5495 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2826 - accuracy: 0.5497 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 0.9681 - accuracy: 0.6250 35/1563 [..............................] - ETA: 2s - loss: 1.2242 - accuracy: 0.5759 68/1563 [>.............................] - ETA: 2s - loss: 1.2666 - accuracy: 0.5597 101/1563 [>.............................] - ETA: 2s - loss: 1.2546 - accuracy: 0.5597 135/1563 [=>............................] - ETA: 2s - loss: 1.2530 - accuracy: 0.5632 170/1563 [==>...........................] - ETA: 2s - loss: 1.2588 - accuracy: 0.5592 204/1563 [==>...........................] - ETA: 2s - loss: 1.2623 - accuracy: 0.5587 238/1563 [===>..........................] - 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ETA: 0s - loss: 1.2563 - accuracy: 0.5609 1088/1563 [===================>..........] - ETA: 0s - loss: 1.2566 - accuracy: 0.5605 1122/1563 [====================>.........] - ETA: 0s - loss: 1.2567 - accuracy: 0.5605 1157/1563 [=====================>........] - ETA: 0s - loss: 1.2555 - accuracy: 0.5606 1192/1563 [=====================>........] - ETA: 0s - loss: 1.2564 - accuracy: 0.5599 1226/1563 [======================>.......] - ETA: 0s - loss: 1.2553 - accuracy: 0.5605 1260/1563 [=======================>......] - ETA: 0s - loss: 1.2546 - accuracy: 0.5605 1294/1563 [=======================>......] - ETA: 0s - loss: 1.2554 - accuracy: 0.5601 1328/1563 [========================>.....] - ETA: 0s - loss: 1.2544 - accuracy: 0.5604 1362/1563 [=========================>....] - ETA: 0s - loss: 1.2559 - accuracy: 0.5598 1396/1563 [=========================>....] - ETA: 0s - loss: 1.2567 - accuracy: 0.5590 1431/1563 [==========================>...] - ETA: 0s - loss: 1.2564 - accuracy: 0.5591 1465/1563 [===========================>..] - ETA: 0s - loss: 1.2542 - accuracy: 0.5601 1499/1563 [===========================>..] - ETA: 0s - loss: 1.2549 - accuracy: 0.5601 1530/1563 [============================>.] - ETA: 0s - loss: 1.2551 - accuracy: 0.5600 1563/1563 [==============================] - ETA: 0s - loss: 1.2555 - accuracy: 0.5597 1563/1563 [==============================] - 2s 1ms/step - loss: 1.2555 - accuracy: 0.5597 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)= 29.965457439422607 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins17348004348403289969.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