IBM has announced a significant new release of its PowerAI deep learning software distribution on Power Systems that attacks the major challenges facing data scientists and developers by simplifying the development experience with tools and data preparation while also dramatically reducing the time required for AI system training from weeks to hours.
Data scientists and developers use deep learning to develop applications ranging from computer vision for self-driving cars to real time fraud detection and credit risk analysis systems. These cognitive applications are much more compute resource hungry than traditional applications and can often overwhelm x86 systems.
“IBM PowerAI on Power servers with GPU accelerators provide at least twice the performance of our x86 platform; everything is faster and easier: adding memory, setting up new servers and so on,” said current PowerAI customer Ari Juntunen, CTO at Elinar Oy Ltd. “As a result, we can get new solutions to market quickly, protecting our edge over the competition. We think that the combination of IBM Power and PowerAI is the best platform for AI developers in the market today. For AI, speed is everything —nothing else comes close in our opinion.”
The new PowerAI roadmap announced today offers four significant new features that address critical customer needs for AI system performance, effective data preparation, and enterprise-level software:
- Ease of Use: A new software tool called “AI Vision” that an application developer can use with limited knowledge about deep learning to train and deploy deep learning models targeted at computer vision for their application needs.
- Tools for data preparation: Integration with IBM Spectrum Conductor cluster virtualization software that integrates Apache Spark to ease the process of transforming unstructured as well as structured data sets to prepare them for deep learning training
- Decreased training time: A distributed computing version of TensorFlow, a popular open-source machine learning framework first built by Google. This distributed version of TensorFlow takes advantage of a virtualized cluster of GPU-accelerated servers using cost-efficient, high-performance computing methods to bring deep learning training time down from weeks to hours
- Easier model development: A new software tool called “DL Insight” that enables data scientists to rapidly get better accuracy from their deep learning models. This tool monitors the deep learning training process and automatically adjusts parameters for peak performance.
Read the source article at IBM – United States.