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 84094 queued and waiting for resources srun: job 84094 has been allocated resources 2024-10-07 07:43:07.578891: 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. 2024-10-07 07:43:31.542342: 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 2024-10-07 07:44:05.393682: 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 2024-10-07 07:44:05.394470: 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 2024-10-07 07:44:05.394478: 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. 2024-10-07 07:44:57.939370: 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. 2024-10-07 07:45:00.557328: 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 2024-10-07 07:45:08.506396: 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: 1.4810 - accuracy: 0.4771 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.2259 - accuracy: 0.5312 40/1563 [..............................] - ETA: 2s - loss: 1.4304 - accuracy: 0.4852 79/1563 [>.............................] - ETA: 1s - loss: 1.4179 - accuracy: 0.4834 118/1563 [=>............................] - ETA: 1s - loss: 1.4218 - accuracy: 0.4915 157/1563 [==>...........................] - ETA: 1s - loss: 1.4149 - accuracy: 0.4974 196/1563 [==>...........................] - ETA: 1s - loss: 1.4232 - accuracy: 0.4954 235/1563 [===>..........................] - ETA: 1s - loss: 1.4226 - accuracy: 0.4961 274/1563 [====>.........................] - ETA: 1s - loss: 1.4244 - accuracy: 0.4951 311/1563 [====>.........................] - ETA: 1s - loss: 1.4328 - accuracy: 0.4940 348/1563 [=====>........................] - ETA: 1s - loss: 1.4371 - accuracy: 0.4943 385/1563 [======>.......................] - ETA: 1s - loss: 1.4381 - accuracy: 0.4927 422/1563 [=======>......................] - 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2s 1ms/step - loss: 1.3864 - accuracy: 0.5127 Epoch 7/10 1/1563 [..............................] - ETA: 1s - loss: 1.6256 - accuracy: 0.5000 38/1563 [..............................] - ETA: 2s - loss: 1.3111 - accuracy: 0.5452 77/1563 [>.............................] - ETA: 1s - loss: 1.3418 - accuracy: 0.5353 116/1563 [=>............................] - ETA: 1s - loss: 1.3553 - accuracy: 0.5202 155/1563 [=>............................] - ETA: 1s - loss: 1.3563 - accuracy: 0.5212 194/1563 [==>...........................] - ETA: 1s - loss: 1.3492 - accuracy: 0.5238 233/1563 [===>..........................] - ETA: 1s - loss: 1.3472 - accuracy: 0.5271 272/1563 [====>.........................] - ETA: 1s - loss: 1.3507 - accuracy: 0.5262 310/1563 [====>.........................] - ETA: 1s - loss: 1.3489 - accuracy: 0.5260 348/1563 [=====>........................] - ETA: 1s - loss: 1.3503 - accuracy: 0.5250 387/1563 [======>.......................] - ETA: 1s - loss: 1.3445 - accuracy: 0.5285 426/1563 [=======>......................] - 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2s 1ms/step - loss: 1.3163 - accuracy: 0.5390 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.4140 - accuracy: 0.5625 39/1563 [..............................] - ETA: 2s - loss: 1.3269 - accuracy: 0.5489 77/1563 [>.............................] - ETA: 1s - loss: 1.3022 - accuracy: 0.5556 116/1563 [=>............................] - ETA: 1s - loss: 1.2908 - accuracy: 0.5552 154/1563 [=>............................] - ETA: 1s - loss: 1.2696 - accuracy: 0.5623 192/1563 [==>...........................] - ETA: 1s - loss: 1.2804 - accuracy: 0.5535 230/1563 [===>..........................] - ETA: 1s - loss: 1.2791 - accuracy: 0.5534 268/1563 [====>.........................] - ETA: 1s - loss: 1.2857 - accuracy: 0.5491 306/1563 [====>.........................] - ETA: 1s - loss: 1.2785 - accuracy: 0.5520 344/1563 [=====>........................] - ETA: 1s - loss: 1.2840 - accuracy: 0.5505 383/1563 [======>.......................] - ETA: 1s - loss: 1.2847 - accuracy: 0.5519 421/1563 [=======>......................] - 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2s 1ms/step - loss: 1.2517 - accuracy: 0.5624 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)= 37.23993635177612 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins722171026689368246.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