Machine learning (ML) needs industry-standard performance benchmarks to
support design and competitive evaluation of the many emerging software and
hardware solutions for ML. But ML training presents three unique benchmarking
challenges absent from other domains: optimizations that improve training
throughput can increase the time to solution, training is stochastic and time
to solution exhibits high variance, and software and hardware systems are so
diverse that fair benchmarking with the same binary, code, and even
hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that
overcomes these challenges. Our analysis quantitatively evaluates MLPerf's
efficacy at driving performance and scalability improvements across two rounds
of results from multiple vendors.

MLPerf 是一个机器学习基准测试，用于量化评估训练性能和可扩展性，解决了优化训练、随机性和系统差异等挑战。