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 84826 queued and waiting for resources srun: job 84826 has been allocated resources 2024-10-14 07:43:07.584084: 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. 2024-10-14 07:43:31.933267: 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 2024-10-14 07:44:07.544730: 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 2024-10-14 07:44:07.545619: 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 2024-10-14 07:44:07.545629: 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. 2024-10-14 07:45:02.424261: 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. 2024-10-14 07:45:04.895251: 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 2024-10-14 07:45:11.941333: 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: 1.6242 - accuracy: 0.4277 Epoch 3/10 1/1563 [..............................] - ETA: 2s - loss: 1.6692 - accuracy: 0.4375 35/1563 [..............................] - ETA: 2s - loss: 1.5419 - accuracy: 0.4688 69/1563 [>.............................] - ETA: 2s - loss: 1.5227 - accuracy: 0.4719 104/1563 [>.............................] - ETA: 2s - loss: 1.5385 - accuracy: 0.4603 138/1563 [=>............................] - ETA: 2s - loss: 1.5609 - accuracy: 0.4493 172/1563 [==>...........................] - ETA: 2s - loss: 1.5525 - accuracy: 0.4524 206/1563 [==>...........................] - ETA: 2s - loss: 1.5521 - accuracy: 0.4502 240/1563 [===>..........................] - ETA: 1s - loss: 1.5539 - accuracy: 0.4516 274/1563 [====>.........................] - ETA: 1s - loss: 1.5590 - accuracy: 0.4487 308/1563 [====>.........................] - ETA: 1s - loss: 1.5594 - accuracy: 0.4478 342/1563 [=====>........................] - ETA: 1s - loss: 1.5557 - accuracy: 0.4481 375/1563 [======>.......................] - 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2s 2ms/step - loss: 1.5431 - accuracy: 0.4542 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.6841 - accuracy: 0.3125 34/1563 [..............................] - ETA: 2s - loss: 1.4611 - accuracy: 0.4715 67/1563 [>.............................] - ETA: 2s - loss: 1.4949 - accuracy: 0.4594 101/1563 [>.............................] - ETA: 2s - loss: 1.5014 - accuracy: 0.4585 134/1563 [=>............................] - ETA: 2s - loss: 1.4985 - accuracy: 0.4564 169/1563 [==>...........................] - ETA: 2s - loss: 1.4952 - accuracy: 0.4604 203/1563 [==>...........................] - ETA: 2s - loss: 1.4909 - accuracy: 0.4644 237/1563 [===>..........................] - ETA: 1s - loss: 1.4945 - accuracy: 0.4648 270/1563 [====>.........................] - ETA: 1s - loss: 1.4934 - accuracy: 0.4656 303/1563 [====>.........................] - ETA: 1s - loss: 1.4909 - accuracy: 0.4665 336/1563 [=====>........................] - ETA: 1s - loss: 1.4940 - accuracy: 0.4664 370/1563 [======>.......................] - 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2s 2ms/step - loss: 1.4840 - accuracy: 0.4743 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.6434 - accuracy: 0.3750 35/1563 [..............................] - ETA: 2s - loss: 1.5018 - accuracy: 0.4589 69/1563 [>.............................] - ETA: 2s - loss: 1.4536 - accuracy: 0.4764 103/1563 [>.............................] - ETA: 2s - loss: 1.4461 - accuracy: 0.4867 137/1563 [=>............................] - ETA: 2s - loss: 1.4423 - accuracy: 0.4891 171/1563 [==>...........................] - ETA: 2s - loss: 1.4496 - accuracy: 0.4868 205/1563 [==>...........................] - ETA: 2s - loss: 1.4519 - accuracy: 0.4896 239/1563 [===>..........................] - ETA: 1s - loss: 1.4479 - accuracy: 0.4927 274/1563 [====>.........................] - ETA: 1s - loss: 1.4446 - accuracy: 0.4952 307/1563 [====>.........................] - ETA: 1s - loss: 1.4472 - accuracy: 0.4931 341/1563 [=====>........................] - ETA: 1s - loss: 1.4465 - accuracy: 0.4924 375/1563 [======>.......................] - 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ETA: 2s - loss: 1.6149 - accuracy: 0.3438 35/1563 [..............................] - ETA: 2s - loss: 1.4285 - accuracy: 0.4920 69/1563 [>.............................] - ETA: 2s - loss: 1.3999 - accuracy: 0.5050 103/1563 [>.............................] - ETA: 2s - loss: 1.4054 - accuracy: 0.5021 137/1563 [=>............................] - ETA: 2s - loss: 1.4079 - accuracy: 0.5041 171/1563 [==>...........................] - ETA: 2s - loss: 1.4150 - accuracy: 0.5013 205/1563 [==>...........................] - ETA: 2s - loss: 1.4080 - accuracy: 0.5038 239/1563 [===>..........................] - ETA: 1s - loss: 1.4045 - accuracy: 0.5039 273/1563 [====>.........................] - ETA: 1s - loss: 1.4045 - accuracy: 0.5038 307/1563 [====>.........................] - ETA: 1s - loss: 1.4055 - accuracy: 0.5045 341/1563 [=====>........................] - ETA: 1s - loss: 1.4029 - accuracy: 0.5053 373/1563 [======>.......................] - ETA: 1s - loss: 1.4021 - accuracy: 0.5057 408/1563 [======>.......................] - 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ETA: 1s - loss: 1.2546 - accuracy: 0.5606 878/1563 [===============>..............] - ETA: 1s - loss: 1.2570 - accuracy: 0.5602 912/1563 [================>.............] - ETA: 0s - loss: 1.2547 - accuracy: 0.5611 946/1563 [=================>............] - ETA: 0s - loss: 1.2550 - accuracy: 0.5602 980/1563 [=================>............] - ETA: 0s - loss: 1.2554 - accuracy: 0.5599 1014/1563 [==================>...........] - ETA: 0s - loss: 1.2534 - accuracy: 0.5609 1048/1563 [===================>..........] - ETA: 0s - loss: 1.2534 - accuracy: 0.5609 1082/1563 [===================>..........] - ETA: 0s - loss: 1.2544 - accuracy: 0.5605 1116/1563 [====================>.........] - ETA: 0s - loss: 1.2561 - accuracy: 0.5601 1151/1563 [=====================>........] - ETA: 0s - loss: 1.2558 - accuracy: 0.5601 1185/1563 [=====================>........] - ETA: 0s - loss: 1.2547 - accuracy: 0.5602 1219/1563 [======================>.......] - ETA: 0s - loss: 1.2551 - accuracy: 0.5602 1250/1563 [======================>.......] - ETA: 0s - loss: 1.2551 - accuracy: 0.5606 1281/1563 [=======================>......] - ETA: 0s - loss: 1.2553 - accuracy: 0.5608 1314/1563 [========================>.....] - ETA: 0s - loss: 1.2556 - accuracy: 0.5605 1348/1563 [========================>.....] - ETA: 0s - loss: 1.2555 - accuracy: 0.5606 1382/1563 [=========================>....] - ETA: 0s - loss: 1.2569 - accuracy: 0.5601 1416/1563 [==========================>...] - ETA: 0s - loss: 1.2582 - accuracy: 0.5597 1450/1563 [==========================>...] - ETA: 0s - loss: 1.2581 - accuracy: 0.5596 1484/1563 [===========================>..] - ETA: 0s - loss: 1.2570 - accuracy: 0.5600 1518/1563 [============================>.] - ETA: 0s - loss: 1.2560 - accuracy: 0.5599 1552/1563 [============================>.] - ETA: 0s - loss: 1.2559 - accuracy: 0.5600 1563/1563 [==============================] - 2s 2ms/step - loss: 1.2555 - accuracy: 0.5602 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)= 38.96396493911743 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins1150185899867216863.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