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IBM PowerAI 1.6.0
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. |
| Caffe2 | n/a | Caffe2 is a companion to PyTorch. PyTorch is great for experimentation and rapid development, while Caffe2 is aimed at production environments. |
| 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. |
| 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. |
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:
wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/caffe_sample.sb sbatch caffe_sample.sb squeue
Simple Example for Caffe2
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 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.sb script:
wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/caffe2_sample.sb sbatch caffe2_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
Visualization with TensorBoard
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
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 and go back to login node:
python ./mnist-with-summaries.py exit
Batch mode
The same can be accomplished in batch mode using the following tfbd_sample.sb script:
sbatch tfbd_sample.sb 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:
tensorboard --logdir ~/tensorflow/mnist/logs/
Forward the port 6006 on remote machine to the port 16006 on local machine:
ssh -N -f -L localhost:16006:localhost:6006 dmu@hal
Paste the follow address into web browser to start the TensorBoard session:
localhost:16006
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