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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 98815 queued and waiting for resources
srun: job 98815 has been allocated resources
2024-11-29 07:43:02.668278: 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.
2024-11-29 07:43:02.843165: 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
2024-11-29 07:43:03.837593: 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
2024-11-29 07:43:03.837663: 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
2024-11-29 07:43:03.837668: 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.
2024-11-29 07:43:07.058393: 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.
2024-11-29 07:43:07.425613: 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
2024-11-29 07:43:10.212985: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.

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Epoch 2/10

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Epoch 3/10

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Epoch 4/10

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Epoch 5/10

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Epoch 6/10

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Epoch 7/10

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Epoch 8/10

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Epoch 9/10

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Epoch 10/10

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1006/1563 [==================>...........] - ETA: 0s - loss: 1.2589 - accuracy: 0.5569
1039/1563 [==================>...........] - ETA: 0s - loss: 1.2583 - accuracy: 0.5570
1073/1563 [===================>..........] - ETA: 0s - loss: 1.2583 - accuracy: 0.5573
1105/1563 [====================>.........] - ETA: 0s - loss: 1.2560 - accuracy: 0.5585
1139/1563 [====================>.........] - ETA: 0s - loss: 1.2554 - accuracy: 0.5588
1173/1563 [=====================>........] - ETA: 0s - loss: 1.2549 - accuracy: 0.5592
1206/1563 [======================>.......] - ETA: 0s - loss: 1.2553 - accuracy: 0.5591
1239/1563 [======================>.......] - ETA: 0s - loss: 1.2550 - accuracy: 0.5595
1272/1563 [=======================>......] - ETA: 0s - loss: 1.2544 - accuracy: 0.5600
1306/1563 [========================>.....] - ETA: 0s - loss: 1.2543 - accuracy: 0.5596
1339/1563 [========================>.....] - ETA: 0s - loss: 1.2539 - accuracy: 0.5596
1372/1563 [=========================>....] - ETA: 0s - loss: 1.2542 - accuracy: 0.5598
1406/1563 [=========================>....] - ETA: 0s - loss: 1.2534 - accuracy: 0.5605
1440/1563 [==========================>...] - ETA: 0s - loss: 1.2530 - accuracy: 0.5604
1474/1563 [===========================>..] - ETA: 0s - loss: 1.2545 - accuracy: 0.5597
1508/1563 [===========================>..] - ETA: 0s - loss: 1.2558 - accuracy: 0.5595
1542/1563 [============================>.] - ETA: 0s - loss: 1.2556 - accuracy: 0.5598
1563/1563 [==============================] - 2s 2ms/step - loss: 1.2558 - accuracy: 0.5597
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)=  27.144286632537842
hydro03

[SSH] completed
[SSH] exit-status: 0

[workspace] $ /bin/sh -xe /tmp/jenkins2489508651338568840.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