TensorFlow Guide
TensorFlow Guide
  1. Deploy
    1. Deploy
    2. Distributed TensorFlow
    3. How to run TensorFlow on Hadoop
    4. TensorFlow Serving
  2. Extend
    1. A Tool Developer's Guide to TensorFlow Model Files
    2. Adding a Custom Filesystem Plugin
    3. Adding a New Op
    4. Creating Estimators in tf.contrib.learn
    5. Custom Data Readers
    6. Extend
    7. TensorFlow Architecture
    8. TensorFlow in other languages
  3. Get Started
    1. Building Input Functions with tf.contrib.learn
    2. Deep MNIST for Experts
    3. Getting Started
    4. Logging and Monitoring Basics with tf.contrib.learn
    5. MNIST For ML Beginners
    6. TensorBoard Histogram Dashboard
    7. TensorBoard: Embedding Visualization
    8. TensorBoard: Graph Visualization
    9. TensorBoard: Visualizing Learning
    10. TensorFlow Mechanics 101
    11. tf.contrib.learn Quickstart
  4. Guide
    1. Exporting and Importing a MetaGraph
    2. Frequently Asked Questions
    3. How to Use TensorFlow Debugger with tf.contrib.learn
    4. Programmer's Guide
    5. Reading data
    6. SavedModel CLI
    7. Sharing Variables
    8. Supervisor: Training Helper for Days-Long Trainings
    9. Tensor Ranks, Shapes, and Types
    10. TensorFlow Data Versioning: GraphDefs and Checkpoints
    11. TensorFlow Debugger Command-Line-Interface Tutorial: MNIST
    12. TensorFlow Version Semantics
    13. Threading and Queues
    14. Variables: Creation, Initialization, Saving, and Loading
  5. Index
    1. index
  6. Performance
    1. Benchmarks
    2. Broadcasting semantics
    3. Developing a new backend for XLA
    4. High-Performance Models
    5. How to Quantize Neural Networks with TensorFlow
    6. Operation Semantics
    7. Performance
    8. Performance Guide
    9. Shapes and Layout
    10. Using AOT compilation
    11. Using JIT Compilation
    12. XLA Overview
  7. Tutorials
    1. A Guide to TF Layers: Building a Convolutional Neural Network
    2. Convolutional Neural Networks
    3. How to Retrain Inception's Final Layer for New Categories
    4. Image Recognition
    5. Large-scale Linear Models with TensorFlow
    6. Mandelbrot Set
    7. Partial Differential Equations
    8. Recurrent Neural Networks
    9. Sequence-to-Sequence Models
    10. TensorFlow Linear Model Tutorial
    11. TensorFlow Wide & Deep Learning Tutorial
    12. Tutorials
    13. Using GPUs
    14. Vector Representations of Words
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TensorFlow Architecture

TensorFlow Architecture

We designed TensorFlow for large-scale distributed training and inference, but it is also flexible enough to support experimentation with new machine learning models and system-level optimizations.

This document describes the system architecture that makes possible this combination of scale and flexibility. It assumes that you have basic familiarity with TensorFlow programming concepts such as the computation graph, operations, and sessions. See Getting Started for an introduction to these topics. Some familiarity with distributed TensorFlow will also be helpful.

This document is for developers who want to extend TensorFlow in some way not supported by current APIs, hardware engineers who want to optimize for TensorFlow, implementers of machine learning systems working on scal