tensorflow 架构

Tensorflow是一个跨平台库。C API之上兼容很多不同的编程语言。

  • Client:
    • Defines the computation as a dataflow graph.
    • Initiates graph execution using a session.
  • Distributed Master
    • Prunes a specific subgraph from the graph, as defined by the arguments to Session.run().
    • Partitions the subgraph into multiple pieces that run in different processes and devices.
    • Distributes the graph pieces to worker services.
    • Initiates graph piece execution by worker services.
  • Worker Services (one for each task)
    • Schedule the execution of graph operations using kernel implementations appropriate to the available hardware (CPUs, GPUs, etc).
    • Send and receive operation results to and from other worker services.
  • Kernel Implementations
    • Perform the computation for individual graph operations.



  • layers module provides a high-level API that makes it easy to construct a neural network

High Level APIs

  • Eager Execution, which is the easiest way to use tensorflow.
  • Estimators, which introduces a high-level TensorFlow API that greatly simplifies ML programming.
  • Importing Data, which explains how to set up data pipelines to read data sets into your TensorFlow program.


  • TensorFlow Architecture | 官方
  • programmers guide | 官方