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 84454 queued and waiting for resources srun: job 84454 has been allocated resources 2024-10-10 07:43:27.239526: 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. 2024-10-10 07:43:51.255620: 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 2024-10-10 07:44:24.643375: 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 2024-10-10 07:44:24.644027: 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 2024-10-10 07:44:24.644036: 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. 2024-10-10 07:45:16.815641: 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. 2024-10-10 07:45:19.436644: 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 2024-10-10 07:45:26.926703: 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: 1.5414 - accuracy: 0.4540 Epoch 4/10 1/1563 [..............................] - ETA: 2s - loss: 1.4122 - accuracy: 0.5938 35/1563 [..............................] - ETA: 2s - loss: 1.5162 - accuracy: 0.4732 69/1563 [>.............................] - ETA: 2s - loss: 1.5153 - accuracy: 0.4751 103/1563 [>.............................] - ETA: 2s - loss: 1.5110 - accuracy: 0.4754 137/1563 [=>............................] - ETA: 2s - loss: 1.5078 - accuracy: 0.4738 171/1563 [==>...........................] - ETA: 2s - loss: 1.5036 - accuracy: 0.4759 205/1563 [==>...........................] - ETA: 2s - loss: 1.5036 - accuracy: 0.4747 239/1563 [===>..........................] - ETA: 1s - loss: 1.5007 - accuracy: 0.4771 273/1563 [====>.........................] - ETA: 1s - loss: 1.4993 - accuracy: 0.4778 307/1563 [====>.........................] - ETA: 1s - loss: 1.4991 - accuracy: 0.4767 341/1563 [=====>........................] - ETA: 1s - loss: 1.4968 - accuracy: 0.4786 375/1563 [======>.......................] - 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2s 2ms/step - loss: 1.4813 - accuracy: 0.4805 Epoch 5/10 1/1563 [..............................] - ETA: 2s - loss: 1.6704 - accuracy: 0.4375 34/1563 [..............................] - ETA: 2s - loss: 1.4643 - accuracy: 0.4954 68/1563 [>.............................] - ETA: 2s - loss: 1.4383 - accuracy: 0.5078 102/1563 [>.............................] - ETA: 2s - loss: 1.4532 - accuracy: 0.4979 136/1563 [=>............................] - ETA: 2s - loss: 1.4483 - accuracy: 0.4989 170/1563 [==>...........................] - ETA: 2s - loss: 1.4598 - accuracy: 0.4926 204/1563 [==>...........................] - ETA: 2s - loss: 1.4643 - accuracy: 0.4913 238/1563 [===>..........................] - ETA: 1s - loss: 1.4564 - accuracy: 0.4933 271/1563 [====>.........................] - ETA: 1s - loss: 1.4580 - accuracy: 0.4915 304/1563 [====>.........................] - ETA: 1s - loss: 1.4563 - accuracy: 0.4927 337/1563 [=====>........................] - ETA: 1s - loss: 1.4508 - accuracy: 0.4955 371/1563 [======>.......................] - 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2s 2ms/step - loss: 1.4319 - accuracy: 0.4963 Epoch 6/10 1/1563 [..............................] - ETA: 2s - loss: 1.7167 - accuracy: 0.4062 34/1563 [..............................] - ETA: 2s - loss: 1.3998 - accuracy: 0.4982 67/1563 [>.............................] - ETA: 2s - loss: 1.4072 - accuracy: 0.5009 100/1563 [>.............................] - ETA: 2s - loss: 1.3970 - accuracy: 0.5056 133/1563 [=>............................] - ETA: 2s - loss: 1.3837 - accuracy: 0.5127 167/1563 [==>...........................] - ETA: 2s - loss: 1.3673 - accuracy: 0.5172 201/1563 [==>...........................] - ETA: 2s - loss: 1.3652 - accuracy: 0.5185 235/1563 [===>..........................] - ETA: 2s - loss: 1.3684 - accuracy: 0.5186 268/1563 [====>.........................] - ETA: 1s - loss: 1.3694 - accuracy: 0.5160 301/1563 [====>.........................] - ETA: 1s - loss: 1.3773 - accuracy: 0.5117 334/1563 [=====>........................] - ETA: 1s - loss: 1.3842 - accuracy: 0.5112 367/1563 [======>.......................] - 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ETA: 0s - loss: 1.3884 - accuracy: 0.5111 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3883 - accuracy: 0.5111 Epoch 7/10 1/1563 [..............................] - ETA: 2s - loss: 1.7854 - accuracy: 0.4062 34/1563 [..............................] - ETA: 2s - loss: 1.3605 - accuracy: 0.5138 68/1563 [>.............................] - ETA: 2s - loss: 1.3671 - accuracy: 0.5106 102/1563 [>.............................] - ETA: 2s - loss: 1.3658 - accuracy: 0.5129 136/1563 [=>............................] - ETA: 2s - loss: 1.3733 - accuracy: 0.5113 170/1563 [==>...........................] - ETA: 2s - loss: 1.3696 - accuracy: 0.5132 204/1563 [==>...........................] - ETA: 2s - loss: 1.3657 - accuracy: 0.5139 238/1563 [===>..........................] - ETA: 1s - loss: 1.3633 - accuracy: 0.5164 272/1563 [====>.........................] - ETA: 1s - loss: 1.3587 - accuracy: 0.5163 306/1563 [====>.........................] - ETA: 1s - loss: 1.3582 - accuracy: 0.5188 340/1563 [=====>........................] - 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ETA: 0s - loss: 1.3510 - accuracy: 0.5246 1563/1563 [==============================] - 2s 1ms/step - loss: 1.3510 - accuracy: 0.5246 Epoch 8/10 1/1563 [..............................] - ETA: 2s - loss: 1.3870 - accuracy: 0.5625 34/1563 [..............................] - ETA: 2s - loss: 1.3243 - accuracy: 0.5579 67/1563 [>.............................] - ETA: 2s - loss: 1.3067 - accuracy: 0.5476 100/1563 [>.............................] - ETA: 2s - loss: 1.3214 - accuracy: 0.5469 133/1563 [=>............................] - ETA: 2s - loss: 1.3220 - accuracy: 0.5428 167/1563 [==>...........................] - ETA: 2s - loss: 1.3284 - accuracy: 0.5389 200/1563 [==>...........................] - ETA: 2s - loss: 1.3205 - accuracy: 0.5452 233/1563 [===>..........................] - ETA: 2s - loss: 1.3223 - accuracy: 0.5440 266/1563 [====>.........................] - ETA: 1s - loss: 1.3149 - accuracy: 0.5453 299/1563 [====>.........................] - ETA: 1s - loss: 1.3080 - accuracy: 0.5473 332/1563 [=====>........................] - 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ETA: 0s - loss: 1.3174 - accuracy: 0.5378 1561/1563 [============================>.] - ETA: 0s - loss: 1.3172 - accuracy: 0.5380 1563/1563 [==============================] - 2s 2ms/step - loss: 1.3172 - accuracy: 0.5379 Epoch 9/10 1/1563 [..............................] - ETA: 2s - loss: 1.2903 - accuracy: 0.5938 35/1563 [..............................] - ETA: 2s - loss: 1.2799 - accuracy: 0.5393 69/1563 [>.............................] - ETA: 2s - loss: 1.2751 - accuracy: 0.5498 103/1563 [>.............................] - ETA: 2s - loss: 1.2725 - accuracy: 0.5534 137/1563 [=>............................] - ETA: 2s - loss: 1.2814 - accuracy: 0.5463 171/1563 [==>...........................] - ETA: 2s - loss: 1.2718 - accuracy: 0.5515 205/1563 [==>...........................] - ETA: 2s - loss: 1.2745 - accuracy: 0.5530 239/1563 [===>..........................] - ETA: 1s - loss: 1.2717 - accuracy: 0.5543 273/1563 [====>.........................] - ETA: 1s - loss: 1.2778 - accuracy: 0.5538 307/1563 [====>.........................] - 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ETA: 0s - loss: 1.2841 - accuracy: 0.5496 1563/1563 [==============================] - 2s 1ms/step - loss: 1.2839 - accuracy: 0.5495 Epoch 10/10 1/1563 [..............................] - ETA: 2s - loss: 1.6743 - accuracy: 0.5625 35/1563 [..............................] - ETA: 2s - loss: 1.2042 - accuracy: 0.5723 69/1563 [>.............................] - ETA: 2s - loss: 1.2134 - accuracy: 0.5743 103/1563 [>.............................] - ETA: 2s - loss: 1.2185 - accuracy: 0.5713 137/1563 [=>............................] - ETA: 2s - loss: 1.2313 - accuracy: 0.5652 171/1563 [==>...........................] - ETA: 2s - loss: 1.2299 - accuracy: 0.5674 205/1563 [==>...........................] - ETA: 2s - loss: 1.2242 - accuracy: 0.5686 239/1563 [===>..........................] - ETA: 1s - loss: 1.2270 - accuracy: 0.5662 273/1563 [====>.........................] - ETA: 1s - loss: 1.2322 - accuracy: 0.5627 307/1563 [====>.........................] - ETA: 1s - loss: 1.2318 - accuracy: 0.5631 340/1563 [=====>........................] - 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2s 1ms/step - loss: 1.2521 - accuracy: 0.5601 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)= 40.259965658187866 hydro05 [SSH] completed [SSH] exit-status: 0 [workspace] $ /bin/sh -xe /tmp/jenkins803784670030265778.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