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IBM
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Watson Machine Learning Community Edition (WMLCE-1.6.
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1)
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 | 1.1.0.1 | 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 | 1.1314.10 | 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 with Caffe
Interactive mode
Get one compute node for interactive use:
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srunswrun --partition=debug --pty --nodes=1 --ntasks-per-node=8 --gres=gpu:v100:1 -t 01:30:00 --wait=0 --export=ALL /bin/bashp gpux1 |
Once on the compute node, load PowerAI module using one of these:
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module load powerai/1.6.0-py2.7 # for python2 environment
module load powerai/1.6.0-py3.6 # for python3 environment
module load powerai # python3 environment by default |
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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 with Caffe2
Interactive mode
Get node for interactive use:
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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 with TensorFlow
Interactive mode
Get node for interactive use:
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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:
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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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localhost:16006 |
Simple Example with Pytorch
Interactive mode
Get node for interactive use:
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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 |
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 | 10.1.105 | The NVIDIA® CUDA® Toolkit provides a development environment for creating high performance GPU-accelerated applications. |
| cudnn | 7.5.0+10.1 | The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. |
| h5py | 2.8.0 | The h5py package is a Pythonic interface to the HDF5 binary data format. |
| jupyter | 1.0.0 | Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. |
| matplotlib | 2.2.3 | Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. |
| nccl | 2.4.2 | The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs. |
| numpy | 1.14.5 | NumPy is the fundamental package for scientific computing with Python. |
| opencv | 3.4.2 | OpenCV was designed for computational efficiency and with a strong focus on real-time applications. |
| pytables | 3.4.4 | PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. |
| pytorch | 1.0.1 | PyTorch enables fast, flexible experimentation and efficient production through a hybrid front-end, distributed training, and ecosystem of tools and libraries. |
| scikit-learn | 0.19.1 | Simple and efficient tools for data mining and data analysis. |
| scipy | 1.1.0 | SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering |
| tensorboard | 1.13.0 | To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. |
| tensorflow-gpu | 1.13.1 | The core open source library to help you develop and train ML models. |
| torchvision | 0.2.1 | The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. |
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