IBM Watson Machine Learning Community Edition (WMLCE-1.7.0)
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:
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. |
Pytorch | 1.3.1 | 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 | 2.1.0 | TensorFlow is an end-to-end open-source platform for machine learning. It is developed by Google and by community contributors. |
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
Name | Version | Description |
---|---|---|
caffe | 1.0 | Caffe is a deep learning framework made with expression, speed, and modularity in mind. |
cudatoolkit | 10.2.89 | The NVIDIA® CUDA® Toolkit provides a development environment for creating high-performance GPU-accelerated applications. |
cudnn | 7.6.5+10.2 | The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. |
nccl | 2.5.6 | The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication primitives that are performance-optimized for NVIDIA GPUs. |
opencv | 3.4.8 | OpenCV was designed for computational efficiency and with a strong focus on real-time applications. |
pytorch | 1.3.1 | PyTorch enables fast, flexible experimentation and efficient production through a hybrid front-end, distributed training, and ecosystem of tools and libraries. |
tensorboard | 2.1.0 | To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. |
tensorflow-gpu | 2.1.0 | The core open-source library to help you develop and train ML models. |