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en:services:application_services:high_performance_computing:tensorflow [2021/04/22 15:35]
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-====== TensorFlow ====== 
-[[https://​www.tensorflow.org|TensorFlow]] is a open-source machine learning framework mainly developed by Google. It can be used for verious machine learning tasks, e.g. deep learning. TensorFlow provides a high level API in python and other languages and is can run on CPUs as well as GPUs. 
- 
-==== Installing TensorFlow ==== 
-It is recommended to use Anaconda to create a virtual python environment and install the desired version of tensorflow within that environment. 
-<code bash> 
-module load anaconda3 
-source $ANACONDA3_ROOT/​etc/​profile.d/​conda.sh 
-conda create -n myenv python=3.8.8 
-conda activate myenv 
-conda install tensorflow-gpu==2.2.0 
-</​code>​ 
-If you do not want to use GPUs simply replace the last line with <code bash>​conda install tensorflow==2.2.0</​code>​ 
- 
-==== Testing the installation ==== 
-To run TensorFlow on GPUs, load the correct modules and submit a job to the gpu partition. 
-<file bash jobscript.sh>​ 
-#!/bin/bash 
-#SBATCH -p gpu 
-#SBATCH -t 1 
-#SBATCH --gpus-per-node 1 
- 
-module load anaconda3 
- 
-source $ANACONDA3_ROOT/​etc/​profile.d/​conda.sh 
-conda activate myenv 
-  
-python tftest.py 
-</​file>​ 
- 
-<file python tftest.py>​ 
-import tensorflow as tf 
-tf.compat.v1.disable_eager_execution() 
-hello = tf.constant('​Hello,​ TensorFlow!'​) 
-sess = tf.compat.v1.Session() 
-print(sess.run(hello)) 
-</​file>​ 
-And then submit the job using Slurm: <code bash> 
-sbatch jobscript.sh</​code>​ 
- 
-The output file should contain 
-<​code>​ 
-The output (if any) follows: 
- 
-b'​Hello,​ TensorFlow!'</​code>​ 
-and also information about the GPUs selected. 
- 
-=== Testing CPU only installation === 
-If you want to test a CPU only installation,​ you can just run the tftest.py on a login node. 
- 
-==== Using TensorFlow ==== 
-You can now use TensorFlow in your python scripts. Please read [[en:​services:​application_services:​high_performance_computing:​running_jobs_slurm#​gpu_selection]] for more information about GPU usage.