TensorFlow

TensorFlow

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java. This flexibility lends itself to a range of applications in many different sectors.

Slurm Script

#!/bin/bash

#SBATCH -J mytensorjob # Job name
#SBATCH -o mytensor.%j.out # Name of stdout output file (%j expands to jobId)
#SBATCH -p CUIQue # Queue name
#SBATCH -N 1 # Total number of nodes requested
#SBATCH -n 16 # Total number of mpi tasks requested

module load py-tensorflow/2.7.0 python/3.8.12 py-numpy/1.22.4

python3.8 basicOperations.py

 

basicOperations.py Code

#!/usr/bin/env python
# coding: utf-8
# # Basic Tensor Operations

from __future__ import print_function
import tensorflow as tf

# Define tensor constants.
a = tf.constant(2)
b = tf.constant(3)
c = tf.constant(5)

# Various tensor operations.
# Note: Tensors also support python operators (+, *, ...)
add = tf.add(a, b)
sub = tf.subtract(a, b)
mul = tf.multiply(a, b)
div = tf.divide(a, b)

# Access tensors value.
print("add =", add.numpy())
print("sub =", sub.numpy())
print("mul =", mul.numpy())
print("div =", div.numpy())

# Some more operations.
mean = tf.reduce_mean([a, b, c])
sum = tf.reduce_sum([a, b, c])

# Access tensors value.
print("mean =", mean.numpy())
print("sum =", sum.numpy())

# Matrix multiplications.
matrix1 = tf.constant([[1., 2.], [3., 4.]])
matrix2 = tf.constant([[5., 6.], [7., 8.]])

product = tf.matmul(matrix1, matrix2)
print(product.numpy())

Submit the job to the scheduler as

sbatch pytensorflowexample.slurm

For managing your “job” , refer to this guide.

For more on Tensorflow framework, refer to their official documentation
https://www.tensorflow.org/overview