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Simple Example for TensorFlow
Interactive mode
Get node for interactive use:
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srun --partition=debug --pty --nodes=1 --ntasks-per-node=8 --gres=gpu:v100:1 -t 01:30:00 --wait=0 --export=ALL /bin/bash |
Once on the compute node, load PowerAI module using one of these:
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module load ibm/powerai/1.6.0.py2 # for python2 environment
module load ibm/powerai/1.6.0.py3 # for python3 environment
module load ibm/powerai # python3 environment by default |
Copy the following code into file "mnist-demo.py":
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import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test) |
Train on MNIST with keras API:
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python3 ./mnist-demo.py |
Batch mode
The same can be accomplished in batch mode using the following tf_sample.sb script:
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sbatch tf_sample.sb
squeue |
Simple Example for Pytorch
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