Singularity Tutorial

keras_example.py

import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)

# the data, split between train and test sets
# (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

x_train = np.random.randint(0, 255, (60000, 28, 28, 1))
y_train = np.random.randint(0, 10, (60000, ))
x_test = np.random.randint(0, 255, (300, 28, 28, 1))
y_test = np.random.randint(0, 10, (300, ))

# print(y_train.shape, y_train, x_train)

# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")


# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = keras.Sequential(
    [
        keras.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation="softmax"),
    ]
)

model.summary()

batch_size = 128
epochs = 15

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)

model.save("convnet.h5")

submit.sh

#!/bin/bash

#SBATCH -p dgx       #HPC-partition
#SBATCH -N 1         #number-of-node
#SBATCH -t 00:10:00  #time-to-use
#SBATCH -J keras-singularity-test   #job-name
#SBATCH -A INSERT_PROJECTNAME  #project-account

CUDAxxxx=11

module purge
module load Singularity
singularity exec --nv newsif.sif python keras_example.py