- 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