username@lanta:~> ml av Apptainer
------------------- /lustrefs/disk/modules/easybuild/modules/all --------------------
   Apptainer/1.1.6

If the avail list is too long consider trying:

"module --default avail" or "ml -d av" to just list the default modules.
"module overview" or "ml ov" to display the number of modules for each name.

Use "module spider" to find all possible modules and extensions.
Use "module keyword key1 key2 ..." to search for all possible modules matching any of the "keys".

username@lanta:~> ml Apptainer/1.1.6

username@lanta:~> apptainer pull tensorflow_2.17.0-gpu.sif docker://tensorflow/tensorflow:2.17.0-gpu

# code is taken from https://www.tensorflow.org/tutorials/keras/classification
# Tensorflow version 2.17.0

import tensorflow as tf
import numpy as np

fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

with open('mnist_data.npy', 'wb') as f:
    np.save(f, train_images)
    np.save(f, train_labels)
    np.save(f, test_images)
    np.save(f, test_labels)

# this code is taken from https://www.tensorflow.org/tutorials/keras/classification 
# the code was modified to using pre-downloaded data instead of downloading from the API.
# Tensorflow version 2.17.0

import tensorflow as tf
import numpy as np

fashion_mnist = tf.keras.datasets.fashion_mnist
#(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

with open('mnist_data.npy', 'rb') as f: 
    train_images = np.load(f)
    train_labels = np.load(f)
    test_images = np.load(f)
    test_labels = np.load(f)

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# Explore Data
train_images.shape
len(train_labels)

test_images.shape
len(test_labels)

# Preprocess Data
train_images = train_images / 255.0
test_images = test_images / 255.0

tf.debugging.set_log_device_placement(True)
strategy = tf.distribute.MirroredStrategy()

with strategy.scope() :
    model = tf.keras.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(10)
    ])

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\n Test loss:', test_loss)
print('\nTest accuracy:', test_acc)

# MIT License
#
# Copyright (c) 2017 François Chollet
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.

#!/bin/bash
#SBATCH -p gpu                           # Specify partition [Compute/Memory/GPU]
#SBATCH -N 1 -c 16   			         # Specify number of nodes and processors per task
#SBATCH --gpus-per-node=4		         # Specify number of GPU per task
#SBATCH --ntasks-per-node=4		         # Specify tasks per node
#SBATCH -t 120:00:00                     # Specify maximum time limit (hour: minute: second)
#SBATCH -A ltxxxxxx               	     # Specify project name
#SBATCH -J JOBNAME               	     # Specify job name

module load Apptainer/1.1.6              # Load the Apptainer module
apptainer exec --nv -B $PWD:$PWD tensorflow_2.17.0-gpu.sif python3 MNIST.py       # Run your program

username@lanta:~> apptainer exec -B $PWD:$PWD tensorflow_2.17.0-gpu.sif python Setup.py

username@lanta:~> sbatch Submit.sh