Amazon Web Services has released the code from its SageMaker Neo machine learning service as the Neo-AI project under the Apache Software License. First launched at AWS re:Invent in November, SageMaker Neo aims to help developers optimize trained machine learning models for targeted hardware platforms.
Similarly, Neo-AI will enable chipmakers, device makers and developers to optimize machine learning models for a wide variety of hardware platforms.
AWS said Neo-AI automatically optimizes models for TensorFlow, MXNet, PyTorch, ONNX, and XGBoost frameworks, and potentially doubles the speed of those models without a loss in accuracy. The platform also converts models into a common format to reduce compatibility issues.
- Amazon AWS, Microsoft Azure, and Google Cloud Platform: Comparing prices for basic services (Tech Republic)
- Everything as a Service: Why companies are making the switch to SaaS, IaaS, PaaS, and more (Tech Pro Research)
Overall, AWS said the goal is to accelerate machine learning deployments on edge devices.
"Ordinarily, optimizing a machine learning model for multiple hardware platforms is difficult because developers need to tune models manually for each platform's hardware and software configuration," AWS's Sukwon Kim and Vin Sharma write in a blog post. "This is especially challenging for edge devices, which tend to be constrained in compute power and storage."
Intel, Nvidia, and Arm are supporting the first release of Neo-AI, with Xilinx, Cadence, and Qualcomm set to come on board at a later time.
- What's the best cloud storage for you?
- Top cloud providers 2018: How AWS, Microsoft, Google
- Everything you need to know about the cloud, explained
- XaaS: Why 'everything' is now a service
- Infographic: Why companies are switching to Everything as a Service
- Free PDF download: The Future of Everything as a Service
- SaaS, PaaS, and IaaS: Understand the differences
- Cloud computing: How to make the move without losing control
- Amazon cloud lead shrinks as Microsoft Azure growth explodes TechRepublic