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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:
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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 |
Install samples for Caffe:
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caffe-install-samples ~/caffe-samples cd ~/caffe-samples |
Download data for MNIST model:
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./data/mnist/get_mnist.sh |
Convert data and create MNIST model:
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./examples/mnist/create_mnist.sh |
Train LeNet on MNIST:
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./examples/mnist/train_lenet.sh |
Batch mode
The same can be accomplished in batch mode using the following caffe_sample.sb script:
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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:
| Code Block |
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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 |
Install samples for Caffe2:
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caffe2-install-samples ~/caffe2-samples cd ~/caffe2-samples |
Download data with LMDB:
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python ./examples/lmdb_create_example.py --output_file lmdb |
Train ResNet50 with Caffe2:
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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:
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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:
| Code Block |
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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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python ./mnist-demo.py |
Batch mode
The same can be accomplished in batch mode using the following tf_sample.sb script:
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wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/tf_sample.sb sbatch tf_sample.sb squeue |
Visualization with TensorBoard
Interactive mode
Get node for interactive use:
| Code Block |
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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 |
Download the code mnist-with-summaries.py to $HOME folder:
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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:
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python ./mnist-with-summaries.py exit |
Batch mode
The same can be accomplished in batch mode using the following tfbd_sample.sb script:
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wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/tfbd_sample.sb 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:
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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 16006 on local machine:
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ssh -N -f -L localhost:16006:localhost:[user_pick_port] dmu@hal |
Paste the follow address into web browser to start the TensorBoard session:
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localhost:16006 |
Simple Example for Pytorch
Interactive mode
Get node for interactive use:
| Code Block |
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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:
| Code Block |
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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 |
Install samples for Pytorch:
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pytorch-install-samples ~/pytorch-samples cd ~/pytorch-samples |
Train on MNIST with Pytorch:
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python ./examples/mnist/main.py |
Batch mode
The same can be accomplished in batch mode using the following pytorch_sample.sb script:
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wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/pytorch_sample.sb sbatch pytorch_sample.sb squeue |