MLPerf is an industry consortium established for Machine Learning (ML)/Artificial Intelligence (AI) solution benchmarking and best practices. MLPerf benchmarks enable fair comparison of the training and inference performance of ML/AL hardware, software and algorithms/models.
MLPerf Training measures how fast a system can train machine learning models. This includes image classification, lightweight and heavy-weight object detection, language translation, natural language processing, recommendation and reinforcement learning, each with specific datasets, quality targets and reference implementation models. You can read about the latest Dell MLPerf training performance in my previous blog.The MLPerf Inference suite measures how quickly a trained neural network can evaluate new data and perform forecasting or classification for a wide range of applications. MLPerf Inference includes image classification, object detection and machine translation with specific models, datasets, quality, server latency and multi-stream latency constraints. MLPerf validated and published results for MLPerf Inference v0.7 on October 21, 2020. While all training happens in the data center, Inference is more geographically dispersed. To ensure coverage of all cases, MLPerf is further categorized into a datacenter and edge division.
Dell EMC had a total of 210 submissions for MLPerf Inference v0.7 in the data center category using various Dell EMC platforms and accelerators from leading vendors. We achieved impressive results when compared to other submissions in the same class of platforms. A few of the highlights is are captured below.
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