Training ML is More Capable According to MLPerf Results
Today, MLCommons®, an open engineering consortium, released new results from MLPerf™ Training v2.0, which measures the performance of training machine learning models. Training models empower researchers to unlock new capabilities faster such as diagnosing tumors, automatic speech recognition, or improving movie recommendations. The latest MLPerf Training results demonstrate broad industry participation and up to 1.8X greater performance ultimately paving the way for more capable intelligent systems to benefit society at large.
The MLPerf Training benchmark suite comprises full system tests that stress machine learning models, software, and hardware for a broad range of applications. The open-source and peer-reviewed benchmark suite provides a level playing field for competition that drives innovation, performance, and energy efficiency for the entire industry.
In this round, MLPerf Training added a new object detection benchmark that trains the new RetinaNet reference model on the larger and more diverse OpenImages dataset. This new test more accurately reflects state-of-the-art ML training for applications such as collision avoidance for vehicles and robotics as well as retail analytics.
“I’m excited to release our new object detection benchmark, which was built based on extensive feedback from a customer advisory board and is an excellent tool for purchasing decisions, designing new accelerators and improving software,” said David Kanter, executive director of MLCommons.
The MLPerf Training v2.0 results include over 250 performance results from 21 different submitters including Azure, Baidu, Dell, Fujitsu, GIGABYTE, Google, Graphcore, HPE, Inspur, Intel-HabanaLabs, Lenovo, Nettrix, NVIDIA, Samsung, and Supermicro. In particular, MLCommons would like to congratulate first-time MLPerf Training submitters ASUSTeK, CASIA, H3C, HazyResearch, Krai, and MosaicML.
MLCommons is an open engineering consortium with a mission to benefit society by accelerating innovation in machine learning. The foundation for MLCommons began with the MLPerf benchmark in 2018, which rapidly scaled as a set of industry metrics to measure machine learning performance and promote transparency of machine learning techniques. In collaboration with its 50+ founding partners – global technology providers, academics, and researchers, MLCommons is focused on collaborative engineering work that builds tools for the entire machine learning industry through benchmarks and metrics, public datasets, and best practices.