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See Getting started with Open Cognitive Environment (OpenCE, former WMLCE) for the latest software stack. |
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IBM Watson Machine Learning Community Edition (WMLCE-1.7.0, WMLCE-1.6.2)
WMLCE 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 | 1.03.1 | 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 | 2.1.13.10 | TensorFlow is an end-to-end open-source platform for machine learning. It is developed by Google and by community contributors. |
For complete PowerAI WMLCE documentation, see https://wwwdeveloper.ibm.com/support/knowledgecenter/SS5SF7_1.6.0/navigation/pai_getstarted.htmlinuxonpower/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:
| Code Block |
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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:
| Code Block |
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module load ibmwmlce/powerai/1.6.0.py2 # for python2 environment2 module load ibmwmlce/powerai/1.67.0.py3 # for python3 environment module load ibm/powerai # python3 environment by default |
Install samples for Caffe:
...
| Code Block |
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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 scriptswb script:
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sbatchwget https://wiki.ncsa.illinois.edu/download/attachments/82510352/caffe_sample.swb swbatch caffe_sample.sbswb squeue |
Simple Example
...
with Caffe2
Interactive mode
Get a node for interactive use:
| Code Block |
|---|
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:
| Code Block |
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module load ibmwmlce/powerai/1.6.0.py2 # for python2 environment2 module load ibmwmlce/powerai/1.6.0.py3 # for python3 environment module load ibm/powerai # python3 environment by default7.0 |
Install samples for Caffe2:
| Code Block |
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caffe2-install-samples ~/caffe2-samples cd ~/caffe2-samplesamples |
Download data with LMDB:
| Code Block |
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python ./examples/lmdb_create_example.py --output_file lmdb |
...
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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 scriptswb script:
| Code Block |
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wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/caffe2_sample.swb sbatch caffe2_sample.sbswb squeue |
Simple Example
...
with TensorFlow
Interactive mode
Get a node for interactive use:
| Code Block |
|---|
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:
| Code Block |
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module load ibmwmlce/powerai/1.6.0.py2 # for python2 environment2 module load ibm/poweraiwmlce/1.6.0.py3 # for python3 environment module load ibm/powerai # python3 environment by default7.0 |
Copy the following code into file "mnist-demo.py":
...
| Code Block |
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python ./mnist-demo.py |
Batch mode
The same can be accomplished in batch mode using the following tf_sample.sb scriptswb script:
| Code Block |
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wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/tf_sample.swb sbatch tf_sample.sbswb squeue |
...
Visualization with TensorBoard
Interactive mode
Get a node for interactive use:
| Code Block |
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swrun -p gpux1 |
Once on the compute node, load PowerAI module using one of these:
| Code Block |
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module load wmlce/1.6.2
module load wmlce/1.7.0 |
Download the code mnist-with-summaries.py to $HOME folder:
| Code Block |
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cd ~
wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/mnist-with-summaries.py |
Train on MNIST with TensorFlow summary:
| Code Block |
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python ./mnist-with-summaries.py |
Batch mode
The same can be accomplished in batch mode using the following tfbd_sample.swb script:
| Code Block |
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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 hal-ondemand, detail refers Getting started with HAL OnDemand.
Simple Example with Pytorch
Interactive mode
Get a node for interactive use:
| Code Block |
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swrun -p gpux1srun --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 ibmwmlce/powerai/1.6.0.py2 # for python2 environment2 module load ibm/poweraiwmlce/1.6.0.py3 # for python3 environment module load ibm/powerai # python3 environment by default7.0 |
Install samples for Pytorch:
...
| Code Block |
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python ./examples/mnist/main.py |
Batch mode
The same can be accomplished in batch mode using the following pytorch_sample.sbswb script:
| Code Block |
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wget https://wiki.ncsa.illinois.edu/download/attachments/82510352/pytorch_sample.swb sbatch pytorch_sample.sb squeueswb 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. |