PowerAI is an enterprise software distribution that combines popular open source deep learning frameworks, efficient AI development tools, and accelerated IBM Power Systems servers. It includes the following frameworks:
| Framework | Version | Description |
|---|---|---|
| Caffe | 1.0 | Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors. |
| TensorFlow | 1.13.1 | TensorFlow is an end-to-end open source platform for machine learning. It is developed by Google and by community contributors. |
| Pytorch | 1.0.1 | Pytorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is developed by Facebook and by community contributors. |
For complete PowerAI documentation, see https://www.ibm.com/support/knowledgecenter/SS5SF7_1.6.0/navigation/pai_getstarted.htm. Here we only show simple examples with system-specific instructions.
Simple Example for Caffe
Interactive mode
Get node for interactive use:
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:
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
Install samples for Caffe:
caffe-install-samples ~/caffe-samples cd ~/caffe-samples
Download data for MNIST model:
./data/mnist/get_mnist.sh
Convert data and create MNIST model:
./examples/mnist/create_mnist.sh
Train LeNet on MNIST:
./examples/mnist/train_lenet.sh
Batch mode
The same can be accomplished in batch mode using the following caffe_sample.sb script:
sbatch caffe_sample.sb squeue
Simple Example for TensorFlow
Interactive mode
Get node for interactive use:
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:
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":
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:
python ./mnist-demo.py
Batch mode
The same can be accomplished in batch mode using the following tf_sample.sb script:
sbatch tf_sample.sb squeue
Simple Example for Pytorch
Interactive mode
Get node for interactive use:
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:
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
Install samples for Pytorch:
pytorch-install-samples ~/pytorch-samples cd ~/pytorch-samples
Train on MNIST with Pytorch:
python ./examples/mnist/main.py
Batch mode
The same can be accomplished in batch mode using the following pytorch_sample.sb script:
sbatch pytorch_sample.sb squeue