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 84172 queued and waiting for resources srun: job 84172 has been allocated resources 2024-10-08 07:43:07.659528: 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. 2024-10-08 07:43:32.224669: 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 2024-10-08 07:44:05.263881: 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 2024-10-08 07:44:05.264846: 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 2024-10-08 07:44:05.264855: 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. 2024-10-08 07:44:56.638691: 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. 2024-10-08 07:44:58.828658: 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 2024-10-08 07:45:06.649605: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. 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2s 2ms/step - loss: 1.5405 - accuracy: 0.4567 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.5040 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.5592 - accuracy: 0.4605 67/1563 [>.............................] - ETA: 2s - loss: 1.5596 - accuracy: 0.4566 100/1563 [>.............................] - ETA: 2s - loss: 1.5322 - accuracy: 0.4719 133/1563 [=>............................] - ETA: 2s - loss: 1.5294 - accuracy: 0.4690 166/1563 [==>...........................] - ETA: 2s - loss: 1.5248 - accuracy: 0.4712 199/1563 [==>...........................] - ETA: 2s - loss: 1.5162 - accuracy: 0.4746 232/1563 [===>..........................] - ETA: 2s - loss: 1.5143 - accuracy: 0.4740 264/1563 [====>.........................] - ETA: 1s - loss: 1.5115 - accuracy: 0.4734 297/1563 [====>.........................] - ETA: 1s - loss: 1.5076 - accuracy: 0.4726 330/1563 [=====>........................] - ETA: 1s - loss: 1.5072 - accuracy: 0.4739 363/1563 [=====>........................] - 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ETA: 0s - loss: 1.4826 - accuracy: 0.4807 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4829 - accuracy: 0.4804 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.4061 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.4115 - accuracy: 0.5046 67/1563 [>.............................] - ETA: 2s - loss: 1.4250 - accuracy: 0.5056 101/1563 [>.............................] - ETA: 2s - loss: 1.4319 - accuracy: 0.4972 135/1563 [=>............................] - ETA: 2s - loss: 1.4458 - accuracy: 0.4947 168/1563 [==>...........................] - ETA: 2s - loss: 1.4498 - accuracy: 0.4946 202/1563 [==>...........................] - ETA: 2s - loss: 1.4493 - accuracy: 0.4940 236/1563 [===>..........................] - ETA: 2s - loss: 1.4487 - accuracy: 0.4940 269/1563 [====>.........................] - ETA: 1s - loss: 1.4500 - accuracy: 0.4945 303/1563 [====>.........................] - ETA: 1s - loss: 1.4484 - accuracy: 0.4923 337/1563 [=====>........................] - 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ETA: 0s - loss: 1.4328 - accuracy: 0.4976 1563/1563 [==============================] - 2s 2ms/step - loss: 1.4322 - accuracy: 0.4978 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.4730 - accuracy: 0.4688 34/1563 [..............................] - ETA: 2s - loss: 1.3930 - accuracy: 0.5101 67/1563 [>.............................] - ETA: 2s - loss: 1.3644 - accuracy: 0.5210 100/1563 [>.............................] - ETA: 2s - loss: 1.3835 - accuracy: 0.5134 133/1563 [=>............................] - ETA: 2s - loss: 1.3877 - accuracy: 0.5099 167/1563 [==>...........................] - ETA: 2s - loss: 1.3949 - accuracy: 0.5071 201/1563 [==>...........................] - ETA: 2s - loss: 1.4081 - accuracy: 0.5033 235/1563 [===>..........................] - ETA: 2s - loss: 1.4108 - accuracy: 0.5031 269/1563 [====>.........................] - ETA: 1s - loss: 1.4058 - accuracy: 0.5065 302/1563 [====>.........................] - ETA: 1s - loss: 1.4038 - accuracy: 0.5054 335/1563 [=====>........................] - 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ETA: 0s - loss: 1.3902 - accuracy: 0.5113 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3900 - accuracy: 0.5112 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.2271 - accuracy: 0.5312 34/1563 [..............................] - ETA: 2s - loss: 1.3751 - accuracy: 0.5175 67/1563 [>.............................] - ETA: 2s - loss: 1.3640 - accuracy: 0.5266 100/1563 [>.............................] - ETA: 2s - loss: 1.3615 - accuracy: 0.5263 134/1563 [=>............................] - ETA: 2s - loss: 1.3437 - accuracy: 0.5347 168/1563 [==>...........................] - ETA: 2s - loss: 1.3377 - accuracy: 0.5346 202/1563 [==>...........................] - ETA: 2s - loss: 1.3577 - accuracy: 0.5278 236/1563 [===>..........................] - ETA: 2s - loss: 1.3590 - accuracy: 0.5278 269/1563 [====>.........................] - ETA: 1s - loss: 1.3593 - accuracy: 0.5265 303/1563 [====>.........................] - ETA: 1s - loss: 1.3589 - accuracy: 0.5262 337/1563 [=====>........................] - 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ETA: 0s - loss: 1.3514 - accuracy: 0.5232 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3529 - accuracy: 0.5230 Epoch 8/10 1/1563 [..............................] - ETA: 2s - loss: 1.2238 - accuracy: 0.6875 34/1563 [..............................] - ETA: 2s - loss: 1.3299 - accuracy: 0.5414 67/1563 [>.............................] - ETA: 2s - loss: 1.3413 - accuracy: 0.5368 101/1563 [>.............................] - ETA: 2s - loss: 1.3293 - accuracy: 0.5405 135/1563 [=>............................] - ETA: 2s - loss: 1.3285 - accuracy: 0.5375 168/1563 [==>...........................] - ETA: 2s - loss: 1.3268 - accuracy: 0.5348 202/1563 [==>...........................] - ETA: 2s - loss: 1.3214 - accuracy: 0.5379 236/1563 [===>..........................] - ETA: 2s - loss: 1.3256 - accuracy: 0.5358 269/1563 [====>.........................] - ETA: 1s - loss: 1.3188 - accuracy: 0.5358 303/1563 [====>.........................] - ETA: 1s - loss: 1.3209 - accuracy: 0.5351 336/1563 [=====>........................] - 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ETA: 0s - loss: 1.3151 - accuracy: 0.5364 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3150 - accuracy: 0.5364 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.1233 - accuracy: 0.5938 34/1563 [..............................] - ETA: 2s - loss: 1.2731 - accuracy: 0.5487 68/1563 [>.............................] - ETA: 2s - loss: 1.2681 - accuracy: 0.5547 102/1563 [>.............................] - ETA: 2s - loss: 1.2610 - accuracy: 0.5545 136/1563 [=>............................] - ETA: 2s - loss: 1.2700 - accuracy: 0.5542 169/1563 [==>...........................] - ETA: 2s - loss: 1.2864 - accuracy: 0.5470 202/1563 [==>...........................] - ETA: 2s - loss: 1.2883 - accuracy: 0.5473 235/1563 [===>..........................] - ETA: 2s - loss: 1.2850 - accuracy: 0.5480 268/1563 [====>.........................] - ETA: 1s - loss: 1.2819 - accuracy: 0.5499 301/1563 [====>.........................] - ETA: 1s - loss: 1.2832 - accuracy: 0.5507 334/1563 [=====>........................] - 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ETA: 0s - loss: 1.2819 - accuracy: 0.5487 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2821 - accuracy: 0.5485 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.3410 - accuracy: 0.5000 34/1563 [..............................] - ETA: 2s - loss: 1.2300 - accuracy: 0.5597 68/1563 [>.............................] - ETA: 2s - loss: 1.2440 - accuracy: 0.5584 102/1563 [>.............................] - ETA: 2s - loss: 1.2476 - accuracy: 0.5637 136/1563 [=>............................] - ETA: 2s - loss: 1.2434 - accuracy: 0.5692 170/1563 [==>...........................] - ETA: 2s - loss: 1.2346 - accuracy: 0.5730 203/1563 [==>...........................] - ETA: 2s - loss: 1.2279 - accuracy: 0.5750 236/1563 [===>..........................] - ETA: 2s - loss: 1.2326 - accuracy: 0.5715 269/1563 [====>.........................] - ETA: 1s - loss: 1.2449 - accuracy: 0.5676 302/1563 [====>.........................] - ETA: 1s - loss: 1.2470 - accuracy: 0.5665 336/1563 [=====>........................] - 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ETA: 0s - loss: 1.2522 - accuracy: 0.5622 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2519 - 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)= 40.256972312927246 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins4391106613026717546.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