对果M2和M1处理器在各种机器学习任务中的可用性进行比较
David Kasperek1, Pawel Antonowicz1, Marek Baranowski1
1Department of Computer Science, Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
果MacBook Pro型号 (M1,M1 Pro,M2,M2 Pro) 在机器学习任务中进行了基准测试. 性能在不同模型中各不相同,突出显示了硬件架构对人工智能研究计算效率的影响.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 机器学习研究需要强大的计算资源.
- 评估硬件性能对于优化研究工作流程至关重要.
- 果的M系列芯片为各种计算任务提供集成解决方案.
研究的目的:
- 为了比较不同果MacBook Pro型号 (M1,M1 Pro,M2,M2 Pro) 的性能,用于机器学习研究.
- 评估硬件架构对机器学习任务效率的影响.
- 为选择合适的AI开发硬件提供数据驱动的洞察力.
主要方法:
- 通过使用Swift脚本和Create ML框架进行了四款MacBook Pro模型的基准测试.
- 机器学习模型在基于文本,基于视觉和表格数据的基础上进行训练和评估.
- 记录和分析了主要关注执行时间的性能指标.
主要成果:
- 在M1,M1 Pro,M2 和 M2 Pro MacBook Pro 模型中,性能差异很大.
- 与M1和M2变体相比,M2 Pro在大多数机器学习基准测试中表现优异.
- 硬件架构,特别是芯片规格,直接影响了模型训练和评估所需的时间.
结论:
- 选择MacBook Pro型号显著影响机器学习研究效率.
- 高级的M系列芯片 (例如M2 Pro) 为人工智能工作负载提供了可观的性能提升.
- 硬件选择是优化机器学习研究生产力的关键因素.
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