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 98859 queued and waiting for resources srun: job 98859 has been allocated resources 2024-11-30 07:43:03.120838: 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. 2024-11-30 07:43:03.433093: 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 2024-11-30 07:43:04.663662: 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 2024-11-30 07:43:04.665080: 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 2024-11-30 07:43:04.665088: 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. 2024-11-30 07:43:08.342289: 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. 2024-11-30 07:43:08.767043: 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 2024-11-30 07:43:10.842368: 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: 1.6262 - accuracy: 0.4259 Epoch 3/10 1/1563 [..............................] - ETA: 2s - loss: 1.3161 - accuracy: 0.5938 35/1563 [..............................] - ETA: 2s - loss: 1.5489 - accuracy: 0.4509 69/1563 [>.............................] - ETA: 2s - loss: 1.5838 - accuracy: 0.4393 102/1563 [>.............................] - ETA: 2s - loss: 1.5721 - accuracy: 0.4427 136/1563 [=>............................] - ETA: 2s - loss: 1.5637 - accuracy: 0.4465 170/1563 [==>...........................] - ETA: 2s - loss: 1.5640 - accuracy: 0.4449 204/1563 [==>...........................] - ETA: 2s - loss: 1.5707 - accuracy: 0.4406 238/1563 [===>..........................] - ETA: 1s - loss: 1.5676 - accuracy: 0.4425 271/1563 [====>.........................] - ETA: 1s - loss: 1.5649 - accuracy: 0.4438 304/1563 [====>.........................] - ETA: 1s - loss: 1.5678 - accuracy: 0.4432 338/1563 [=====>........................] - ETA: 1s - loss: 1.5722 - accuracy: 0.4431 372/1563 [======>.......................] - 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2s 2ms/step - loss: 1.5470 - accuracy: 0.4557 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.7243 - accuracy: 0.3750 34/1563 [..............................] - ETA: 2s - loss: 1.4798 - accuracy: 0.4853 67/1563 [>.............................] - ETA: 2s - loss: 1.4837 - accuracy: 0.4832 100/1563 [>.............................] - ETA: 2s - loss: 1.4848 - accuracy: 0.4797 133/1563 [=>............................] - ETA: 2s - loss: 1.4778 - accuracy: 0.4803 167/1563 [==>...........................] - ETA: 2s - loss: 1.4761 - accuracy: 0.4811 201/1563 [==>...........................] - ETA: 2s - loss: 1.4842 - accuracy: 0.4803 234/1563 [===>..........................] - ETA: 2s - loss: 1.4779 - accuracy: 0.4842 268/1563 [====>.........................] - ETA: 1s - loss: 1.4798 - accuracy: 0.4805 301/1563 [====>.........................] - ETA: 1s - loss: 1.4770 - accuracy: 0.4812 335/1563 [=====>........................] - ETA: 1s - loss: 1.4817 - accuracy: 0.4786 369/1563 [======>.......................] - 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2s 2ms/step - loss: 1.4866 - accuracy: 0.4768 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.4645 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.4894 - accuracy: 0.4752 67/1563 [>.............................] - ETA: 2s - loss: 1.4750 - accuracy: 0.4860 101/1563 [>.............................] - ETA: 2s - loss: 1.4595 - accuracy: 0.4916 135/1563 [=>............................] - ETA: 2s - loss: 1.4526 - accuracy: 0.4898 168/1563 [==>...........................] - ETA: 2s - loss: 1.4498 - accuracy: 0.4883 202/1563 [==>...........................] - ETA: 2s - loss: 1.4477 - accuracy: 0.4906 236/1563 [===>..........................] - ETA: 2s - loss: 1.4485 - accuracy: 0.4919 269/1563 [====>.........................] - ETA: 1s - loss: 1.4481 - accuracy: 0.4902 303/1563 [====>.........................] - ETA: 1s - loss: 1.4479 - accuracy: 0.4895 336/1563 [=====>........................] - ETA: 1s - loss: 1.4478 - accuracy: 0.4926 369/1563 [======>.......................] - 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2s 2ms/step - loss: 1.4348 - accuracy: 0.4955 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.5298 - accuracy: 0.4688 35/1563 [..............................] - ETA: 2s - loss: 1.4147 - accuracy: 0.5205 69/1563 [>.............................] - ETA: 2s - loss: 1.4084 - accuracy: 0.5208 102/1563 [>.............................] - ETA: 2s - loss: 1.3997 - accuracy: 0.5208 136/1563 [=>............................] - ETA: 2s - loss: 1.3950 - accuracy: 0.5202 170/1563 [==>...........................] - ETA: 2s - loss: 1.3959 - accuracy: 0.5173 204/1563 [==>...........................] - ETA: 2s - loss: 1.3969 - accuracy: 0.5167 238/1563 [===>..........................] - ETA: 1s - loss: 1.3910 - accuracy: 0.5184 272/1563 [====>.........................] - ETA: 1s - loss: 1.3910 - accuracy: 0.5170 306/1563 [====>.........................] - ETA: 1s - loss: 1.3906 - accuracy: 0.5165 339/1563 [=====>........................] - ETA: 1s - loss: 1.3958 - accuracy: 0.5148 373/1563 [======>.......................] - 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2s 2ms/step - loss: 1.3932 - accuracy: 0.5114 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.6841 - accuracy: 0.4062 34/1563 [..............................] - ETA: 2s - loss: 1.3630 - accuracy: 0.5018 66/1563 [>.............................] - ETA: 2s - loss: 1.3528 - accuracy: 0.5251 99/1563 [>.............................] - ETA: 2s - loss: 1.3571 - accuracy: 0.5211 132/1563 [=>............................] - ETA: 2s - loss: 1.3692 - accuracy: 0.5156 166/1563 [==>...........................] - ETA: 2s - loss: 1.3733 - accuracy: 0.5124 199/1563 [==>...........................] - ETA: 2s - loss: 1.3684 - accuracy: 0.5209 233/1563 [===>..........................] - ETA: 2s - loss: 1.3713 - accuracy: 0.5200 267/1563 [====>.........................] - ETA: 1s - loss: 1.3709 - accuracy: 0.5177 300/1563 [====>.........................] - ETA: 1s - loss: 1.3707 - accuracy: 0.5193 333/1563 [=====>........................] - ETA: 1s - loss: 1.3686 - accuracy: 0.5195 366/1563 [======>.......................] - 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ETA: 0s - loss: 1.3549 - accuracy: 0.5250 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3549 - accuracy: 0.5250 Epoch 8/10 1/1563 [..............................] - ETA: 2s - loss: 1.2849 - accuracy: 0.6562 35/1563 [..............................] - ETA: 2s - loss: 1.3514 - accuracy: 0.5348 69/1563 [>.............................] - ETA: 2s - loss: 1.3419 - accuracy: 0.5380 103/1563 [>.............................] - ETA: 2s - loss: 1.3490 - accuracy: 0.5370 137/1563 [=>............................] - ETA: 2s - loss: 1.3448 - accuracy: 0.5347 171/1563 [==>...........................] - ETA: 2s - loss: 1.3402 - accuracy: 0.5312 205/1563 [==>...........................] - ETA: 2s - loss: 1.3283 - accuracy: 0.5383 239/1563 [===>..........................] - ETA: 1s - loss: 1.3258 - accuracy: 0.5373 273/1563 [====>.........................] - ETA: 1s - loss: 1.3207 - accuracy: 0.5387 305/1563 [====>.........................] - ETA: 1s - loss: 1.3204 - accuracy: 0.5400 338/1563 [=====>........................] - 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ETA: 0s - loss: 1.3165 - accuracy: 0.5390 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3169 - accuracy: 0.5388 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.3122 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.2986 - accuracy: 0.5460 67/1563 [>.............................] - ETA: 2s - loss: 1.2872 - accuracy: 0.5448 100/1563 [>.............................] - ETA: 2s - loss: 1.2904 - accuracy: 0.5466 133/1563 [=>............................] - ETA: 2s - loss: 1.2897 - accuracy: 0.5479 166/1563 [==>...........................] - ETA: 2s - loss: 1.2869 - accuracy: 0.5518 199/1563 [==>...........................] - ETA: 2s - loss: 1.2819 - accuracy: 0.5554 233/1563 [===>..........................] - ETA: 2s - loss: 1.2846 - accuracy: 0.5515 266/1563 [====>.........................] - ETA: 1s - loss: 1.2857 - accuracy: 0.5504 299/1563 [====>.........................] - ETA: 1s - loss: 1.2862 - accuracy: 0.5501 333/1563 [=====>........................] - 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ETA: 0s - loss: 1.2865 - accuracy: 0.5486 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2850 - accuracy: 0.5492 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.0620 - accuracy: 0.6250 34/1563 [..............................] - ETA: 2s - loss: 1.2124 - accuracy: 0.5671 67/1563 [>.............................] - ETA: 2s - loss: 1.2369 - accuracy: 0.5681 100/1563 [>.............................] - ETA: 2s - loss: 1.2500 - accuracy: 0.5581 133/1563 [=>............................] - ETA: 2s - loss: 1.2534 - accuracy: 0.5585 166/1563 [==>...........................] - ETA: 2s - loss: 1.2567 - accuracy: 0.5589 199/1563 [==>...........................] - ETA: 2s - loss: 1.2451 - accuracy: 0.5644 232/1563 [===>..........................] - ETA: 2s - loss: 1.2485 - accuracy: 0.5649 265/1563 [====>.........................] - ETA: 1s - loss: 1.2449 - accuracy: 0.5650 298/1563 [====>.........................] - ETA: 1s - loss: 1.2436 - accuracy: 0.5659 331/1563 [=====>........................] - 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ETA: 0s - loss: 1.2551 - accuracy: 0.5592 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2537 - accuracy: 0.5596 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)= 26.520389080047607 hydro03 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins15411203567907242619.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