TensorFlow Overview

Last updated: 2020-07-15 10:40:19

    TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state of the art in machine learning and developers easily build and deploy machine learning-powered applications.

    • Easy model building
      Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.
    • Reliable machine learning production anywhere
      Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use.
    • Powerful experimentation for research
      A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster.

    TensorFlow Architecture

    • Client
      It defines the computation process as a data flow graph and initializes graph execution by using _Session_.
    • Distributed master
      It prunes specific subgraphs in a graph, i.e, parameters defined in Session.run(), partitions a subgraph into multiple parts that run in different processes and devices, and distributes the graph parts to different worker services, which initialize subgraph computation.
    • Worker service (one for each task)
      It schedules the execution of graph operations by using kernel implementations appropriate to the available hardware (CPUs, GPUs, etc.) and sends/receives operation results to/from other worker services.
    • Kernel implementation
      It performs the computation for individual graph operations.

    EMR's Support for TensorFlow

    • TensorFlow version: v1.14.0
    • Currently, TensorFlow can only run on CPU models instead of GPU models.
    • TensorFlowOnSpark can be used for distributed training.

    TensorFlow Development Sample

    Write code: test.py

    import tensorflow as tf
    hello = tf.constant('Hello, TensorFlow!')
    sess = tf.Session()
    print sess.run(hello)
    a = tf.constant(10)
    b = tf.constant(111)
    print sess.run(a+b)
    exit()

    Run the following command:

    python test.py

    For more usage, please visit the TensorFlow official website.

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