高性能TensorFlow模型托管服务器

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报告主题:Advanced model deployments with TensorFlow Serving

报告摘要

TensorFlow服务是TensorFlow生态系统的基石之一。它极大地简化了机器学习模型的部署,并加速了模型的部署。不幸的是,机器学习工程师不熟悉TensorFlow服务的细节,他们错过了显著的性能提升。Hannes Hapke简要介绍了TensorFlow服务,然后深入介绍了高级设置和用例。他介绍了先进的概念和实施建议增加TensorFlow服务设置的性能,其中包括客户如何请求服务器模型的元信息模型,选择最优预测模型优化吞吐量的概述,批处理请求提高吞吐量性能,支持模型的一个示例实现A / B测试,和监控TensorFlow服务设置的概述。

邀请嘉宾

Hannes Hapke是一位机器学习爱好者和谷歌开发专家。他将深度学习应用于各种计算机视觉和自然语言问题,主要兴趣在于机器学习基础设施和自动化模型工作流。Hannes是《自然语言处理在行动中的作用》一书的合著者,他正在为O 'Reilly使用TensorFlow构建机器学习管道。

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Advanced model deployments with TensorFlow Serving Presentation.pdf
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最新论文

The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design. However, insufficient information regarding underlying model provenance and the lack of control over model evolution serve as an impediment to the more widespread adoption of these services in many operational environments which have strict security requirements. Furthermore, tools such as TensorFlow Serving allow models to be deployed as RESTful endpoints, but require error-prone transformations for PyTorch models as these dynamic computational graphs. This is in contrast to the static computational graphs of TensorFlow. To enable rapid deployments of PyTorch models without intermediate transformations we have developed FlexServe, a simple library to deploy multi-model ensembles with flexible batching.

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