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 84282 queued and waiting for resources srun: job 84282 has been allocated resources 2024-10-09 07:43:07.815126: 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. 2024-10-09 07:43:32.997069: 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 2024-10-09 07:44:07.564323: 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 2024-10-09 07:44:07.565199: 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 2024-10-09 07:44:07.565208: 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. 2024-10-09 07:45:03.500190: 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. 2024-10-09 07:45:05.550065: 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 2024-10-09 07:45:13.691160: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. 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2s 1ms/step - loss: 1.4823 - accuracy: 0.4799 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.5043 - accuracy: 0.5000 40/1563 [..............................] - ETA: 2s - loss: 1.4624 - accuracy: 0.4797 78/1563 [>.............................] - ETA: 1s - loss: 1.4676 - accuracy: 0.4892 117/1563 [=>............................] - ETA: 1s - loss: 1.4492 - accuracy: 0.4960 156/1563 [=>............................] - ETA: 1s - loss: 1.4411 - accuracy: 0.4948 195/1563 [==>...........................] - ETA: 1s - loss: 1.4345 - accuracy: 0.4997 234/1563 [===>..........................] - ETA: 1s - loss: 1.4419 - accuracy: 0.4969 273/1563 [====>.........................] - ETA: 1s - loss: 1.4398 - accuracy: 0.4977 311/1563 [====>.........................] - ETA: 1s - loss: 1.4462 - accuracy: 0.4944 349/1563 [=====>........................] - ETA: 1s - loss: 1.4453 - accuracy: 0.4959 387/1563 [======>.......................] - ETA: 1s - loss: 1.4438 - accuracy: 0.4971 426/1563 [=======>......................] - 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2s 1ms/step - loss: 1.3922 - accuracy: 0.5083 Epoch 7/10 1/1563 [..............................] - ETA: 1s - loss: 1.3423 - accuracy: 0.5000 39/1563 [..............................] - ETA: 2s - loss: 1.3362 - accuracy: 0.5296 77/1563 [>.............................] - ETA: 1s - loss: 1.3712 - accuracy: 0.5191 116/1563 [=>............................] - ETA: 1s - loss: 1.3565 - accuracy: 0.5242 155/1563 [=>............................] - ETA: 1s - loss: 1.3596 - accuracy: 0.5282 194/1563 [==>...........................] - ETA: 1s - loss: 1.3613 - accuracy: 0.5261 233/1563 [===>..........................] - ETA: 1s - loss: 1.3585 - accuracy: 0.5275 271/1563 [====>.........................] - ETA: 1s - loss: 1.3635 - accuracy: 0.5271 309/1563 [====>.........................] - ETA: 1s - loss: 1.3594 - accuracy: 0.5270 348/1563 [=====>........................] - ETA: 1s - loss: 1.3546 - accuracy: 0.5282 387/1563 [======>.......................] - ETA: 1s - loss: 1.3569 - accuracy: 0.5257 425/1563 [=======>......................] - 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2s 1ms/step - loss: 1.3190 - accuracy: 0.5378 Epoch 9/10 1/1563 [..............................] - ETA: 1s - loss: 1.1476 - accuracy: 0.6250 39/1563 [..............................] - ETA: 2s - loss: 1.2595 - accuracy: 0.5713 77/1563 [>.............................] - ETA: 1s - loss: 1.3017 - accuracy: 0.5544 116/1563 [=>............................] - ETA: 1s - loss: 1.3063 - accuracy: 0.5501 154/1563 [=>............................] - ETA: 1s - loss: 1.3080 - accuracy: 0.5444 193/1563 [==>...........................] - ETA: 1s - loss: 1.3107 - accuracy: 0.5444 232/1563 [===>..........................] - ETA: 1s - loss: 1.3038 - accuracy: 0.5481 270/1563 [====>.........................] - ETA: 1s - loss: 1.3034 - accuracy: 0.5498 308/1563 [====>.........................] - ETA: 1s - loss: 1.3055 - accuracy: 0.5491 346/1563 [=====>........................] - ETA: 1s - loss: 1.3050 - accuracy: 0.5472 385/1563 [======>.......................] - ETA: 1s - loss: 1.3015 - accuracy: 0.5476 424/1563 [=======>......................] - 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2s 1ms/step - loss: 1.2557 - accuracy: 0.5604 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)= 36.99117040634155 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins7114624440211843418.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