TensorFlow C++ API 中 ClientSession 和 Session 的区别 [英] Difference between ClientSession and Session in TensorFlow C++ API

问题描述

TensorFlow r1.0 C++ API 带有 Session 和 ClientSession 类.TensorFlow 附带的一些示例使用 ClientSession，其他示例使用 Session.这两种不同类型的会话是否在幕后使用相同的底层机制，还是其中一种优于另一种?使用它们的语法有点不同，但除此之外在行为上还有什么不同吗?

TensorFlow r1.0 C++ API comes with Session and ClientSession classes. Some of the examples shipping with TensorFlow use ClientSession and others use Session. Do these two different types of session use the same underlying mechanism under the hood or is one of the preferred over another? The syntax for using them is a bit different but other than that are there any differences in behavior?

推荐答案

在 TensorFlow 的 C++ API 中，tensorflow::Session API 是一个低级接口，用于处理序列化的 GraphDef 协议缓冲区，并为运行子图.

In TensorFlow's C++ API, the tensorflow::Session API is a low-level interface that deals with serialized GraphDef protocol buffers and provides a string-based interface for running subgraphs.

相比之下，tensorflow::ClientSession API 更高级别，并与用于构建 TensorFlow 图的新 C++ API 集成 - 与 Python tf.Graph 和 tf.Session 类可以.

By contrast, the tensorflow::ClientSession API is higher level, and integrates with the new C++ API for building TensorFlow graphs—much in the same way as the Python tf.Graph and tf.Session classes do.

因此，如果您使用 C++ API 构建图形，您可能希望使用 tensorflow::ClientSession，但 tensorflow::Session 接口更容易如果您已经有一个序列化的 GraphDef(代表例如一个预训练的模型)并且只想在该模型上运行推理，请使用.

Therefore, you will probably want to use a tensorflow::ClientSession if you are building the graph with the C++ API, but the tensorflow::Session interface is easier to use if you already have a serialized GraphDef (representing e.g. a pre-trained model) and just want to run inference on that model.

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