B站、支付宝、知乎等头部大厂，都选择通过Alluxio来解决模型训练时效率低、成本高、可靠性低，可扩展性差等诸多难题。
欢迎大家免费下载第四期宝典《PyTorch模型训练性能调优宝典》，了解更多他们的实践经验。本电子书是解决PyTorch训练性能和效率问题的首选宝典。资料目录：
欢迎大家免费下载第四期宝典《PyTorch模型训练性能调优宝典》，了解更多他们的实践经验。本电子书是解决PyTorch训练性能和效率问题的首选宝典。资料目录：
扫码即可免费下载
本宝典中介绍的技术适用于对PyTorch的基础设施及其使用的资源进行调优。这些调优技巧适用于所有模型算法，包括CNNs、RNNs、GANs、transformers（如GPT、BERT）等，且适用于所有领域，如计算机视觉、自然语言处理等。
核心要点：📌
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PyTorch的基础知识，包括张量、计算图、自动微分以及神经网络模块的工作原理；
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影响机器学习流程中模型训练性能的因素；
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优化PyTorch模型训练的分步过程；
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在数据加载、数据操作、GPU处理和CPU处理方面的最佳调优技巧，附有代码示例。通过这些技巧，平均训练epoch时长可缩短至原先的1/5-1/10；
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在真实生产环境中使用Alluxio作为数据访问层为模型训练赋能的案例研究。
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适用对象包括AI/ML平台工程师、数据平台工程师、后端软件工程师、MLOps工程师、站点可靠性工程师、架构师、机器学习工程师以及任何希望掌握PyTorch性能调优技巧的专业人士。
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特别感谢！
翻译支持：Roise，熊迪，Polarish，曹明
特别感谢以上4位Alluxio社区志愿者对《PyTorch模型训练性能调优宝典》翻译工作的支持！
鸣谢
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