Neural network training with TensorFlow on GPU

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Load modules

We will use the new software stack in this tutorial:

[jarunanp@eu-login-11 ~]$ env2lmod  

Load the Python module which contains TensorFlow 2.0.0 package

[jarunanp@eu-login-11 ~]$ module load gcc/6.3.0 python_gpu/3.8.5 eth_proxy

The following have been reloaded with a version change:
  1) gcc/4.8.5 => gcc/6.3.0
[jarunanp@eu-login-11 ~]$ module list

Currently Loaded Modules:
  1) StdEnv            4) cuda/11.0.3    7) python_gpu/3.8.5
  2) gcc/6.3.0         5) cudnn/8.0.5    8) eth_proxy
  3) openblas/0.2.20   6) nccl/2.7.8-1

Check if we could import the TensorFlow package

[jarunanp@eu-login-11 ~]$ python -c "import tensorflow as tf; print(tf.__version__)"

A neural network model

Create a working directory on $SCRATCH

[jarunanp@eu-login-11 ~]$ cd $SCRATCH
[jarunanp@eu-login-11 jarunanp]$ mkdir tf_gpu
[jarunanp@eu-login-11 jarunan]$ cd tf_gpu
[jarunanp@eu-login-11 tf_gpu]$ 

Download the script containing a neural network model which is trained on MNIST dataset. This example is taken from TensorFlow tutorials.

[jarunanp@eu-login-11 tf_gpu]$ wget -O

Request an interactive session on a compute node

[jarunanp@eu-login-11 tf_gpu]$ sbatch -n 1 --gpus=1 --wrap='python'

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