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 98612 queued and waiting for resources srun: job 98612 has been allocated resources 2024-11-25 07:43:03.039140: 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. 2024-11-25 07:43:04.232983: 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 2024-11-25 07:43:07.524782: 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 2024-11-25 07:43:07.526131: 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 2024-11-25 07:43:07.526139: 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. 2024-11-25 07:43:14.731985: 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. 2024-11-25 07:43:16.085614: 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 2024-11-25 07:43:20.232054: 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: 1.4320 - accuracy: 0.4967 1563/1563 [==============================] - 2s 1ms/step - loss: 1.4313 - accuracy: 0.4967 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.6213 - accuracy: 0.5000 37/1563 [..............................] - ETA: 2s - loss: 1.4029 - accuracy: 0.5118 73/1563 [>.............................] - ETA: 2s - loss: 1.3780 - accuracy: 0.5120 109/1563 [=>............................] - ETA: 2s - loss: 1.3684 - accuracy: 0.5169 145/1563 [=>............................] - ETA: 1s - loss: 1.3753 - accuracy: 0.5110 182/1563 [==>...........................] - ETA: 1s - loss: 1.3775 - accuracy: 0.5134 219/1563 [===>..........................] - ETA: 1s - loss: 1.3761 - accuracy: 0.5158 255/1563 [===>..........................] - ETA: 1s - loss: 1.3870 - accuracy: 0.5104 291/1563 [====>.........................] - ETA: 1s - loss: 1.3860 - accuracy: 0.5115 327/1563 [=====>........................] - ETA: 1s - loss: 1.3833 - accuracy: 0.5113 363/1563 [=====>........................] - 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2s 1ms/step - loss: 1.2803 - accuracy: 0.5500 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.3635 - accuracy: 0.5625 37/1563 [..............................] - ETA: 2s - loss: 1.2182 - accuracy: 0.5726 73/1563 [>.............................] - ETA: 2s - loss: 1.2209 - accuracy: 0.5753 109/1563 [=>............................] - ETA: 2s - loss: 1.2404 - accuracy: 0.5662 146/1563 [=>............................] - ETA: 1s - loss: 1.2410 - accuracy: 0.5668 182/1563 [==>...........................] - ETA: 1s - loss: 1.2396 - accuracy: 0.5711 218/1563 [===>..........................] - ETA: 1s - loss: 1.2382 - accuracy: 0.5721 254/1563 [===>..........................] - ETA: 1s - loss: 1.2380 - accuracy: 0.5733 290/1563 [====>.........................] - ETA: 1s - loss: 1.2407 - accuracy: 0.5724 326/1563 [=====>........................] - ETA: 1s - loss: 1.2437 - accuracy: 0.5697 363/1563 [=====>........................] - ETA: 1s - loss: 1.2505 - accuracy: 0.5678 400/1563 [======>.......................] - 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ETA: 0s - loss: 1.2483 - accuracy: 0.5642 1324/1563 [========================>.....] - ETA: 0s - loss: 1.2493 - accuracy: 0.5635 1361/1563 [=========================>....] - ETA: 0s - loss: 1.2486 - accuracy: 0.5637 1398/1563 [=========================>....] - ETA: 0s - loss: 1.2478 - accuracy: 0.5642 1435/1563 [==========================>...] - ETA: 0s - loss: 1.2483 - accuracy: 0.5638 1472/1563 [===========================>..] - ETA: 0s - loss: 1.2489 - accuracy: 0.5632 1509/1563 [===========================>..] - ETA: 0s - loss: 1.2483 - accuracy: 0.5630 1546/1563 [============================>.] - ETA: 0s - loss: 1.2488 - accuracy: 0.5627 1563/1563 [==============================] - 2s 1ms/step - loss: 1.2490 - accuracy: 0.5626 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)= 27.748847246170044 hydro06 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins4733223166524385097.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