Getting started with WMLCE (former PowerAI)

IBM Watson Machine Learning Community Edition (WMLCE-1.7.0, WMLCE-1.6.2)

WMLCE 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:

For complete WMLCE documentation, see https://developer.ibm.com/linuxonpower/deep-learning-powerai/releases/. Here we only show simple examples with system-specific instructions.

Simple Example with Caffe

Interactive mode

Get one compute node for interactive use:

swrun -p gpux1

Once on the compute node, load PowerAI module using one of these:

module load wmlce/1.6.2
module load wmlce/1.7.0

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.swb script:

wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/caffe_sample.swb
swbatch caffe_sample.swb
squeue

Simple Example with Caffe2

Interactive mode

Get a node for interactive use:

swrun -p gpux1

Once on the compute node, load PowerAI module using one of these:

module load wmlce/1.6.2
module load wmlce/1.7.0

Install samples for Caffe2:

caffe2-install-samples ~/caffe2-samples
cd ~/caffe2-samples

Download data with LMDB:

python ./examples/lmdb_create_example.py --output_file lmdb

Train ResNet50 with Caffe2:

python ./examples/resnet50_trainer.py --train_data ./lmdb

Batch mode

The same can be accomplished in batch mode using the following caffe2_sample.swb script:

wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/caffe2_sample.swb
sbatch caffe2_sample.swb
squeue

Simple Example with TensorFlow

Interactive mode

Get a node for interactive use:

swrun -p gpux1

Once on the compute node, load PowerAI module using one of these:

module load wmlce/1.6.2
module load wmlce/1.7.0

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.swb script:

wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/tf_sample.swb
sbatch tf_sample.swb
squeue

Visualization with TensorBoard

Interactive mode

Get a node for interactive use:

swrun -p gpux1

Once on the compute node, load PowerAI module using one of these:

module load wmlce/1.6.2
module load wmlce/1.7.0

Download the code mnist-with-summaries.py to $HOME folder:

cd ~
wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/mnist-with-summaries.py

Train on MNIST with TensorFlow summary:

python ./mnist-with-summaries.py

Batch mode

The same can be accomplished in batch mode using the following tfbd_sample.swb script:

wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/tfbd_sample.swb
sbatch tfbd_sample.swb
squeue

Start the TensorBorad session

After job completed the TensorFlow log files can be found in "~/tensorflow/mnist/logs", start the TensorBoard server on login node:

module load wmlce
tensorboard --logdir ~/tensorflow/mnist/logs/ --port <user_pick_port> # please use random number within [6500-6999]

Forward the <user_pick_port> on remote machine to the port <user_pick_port> on local machine:

ssh -L <user_pick_port>:<node_name>:<user_pick_port> <user_name>@hal.ncsa.illinois.edu

Paste the following address into a web browser to start the TensorBoard session:

localhost:<user_pick_port>

Simple Example with Pytorch

Interactive mode

Get a node for interactive use:

swrun -p gpux1

Once on the compute node, load PowerAI module using one of these:

module load wmlce/1.6.2
module load wmlce/1.7.0

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.swb script:

wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/pytorch_sample.swb
sbatch pytorch_sample.swb
squeue

Major Installed PowerAI Related Anaconda Modules