What is the TensorFlow machine intelligence platform?
TensorFlow is an open source software library for numerical computation using data-flow graphs. It was originally developed by the Google Brain Team within Google‘s Machine Intelligence research organization for machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. It reached version 1.0 in February 2017, and has continued rapid development, with 21,000+ commits thus far, many from outside contributors. This article introduces TensorFlow, its open source community and ecosystem, and highlights some interesting TensorFlow open sourced models.
TensorFlow is cross-platform. It runs on nearly everything: GPUs and CPUs—including mobile and embedded platforms—and even tensor processing units (TPUs, which are specialized hardware to do tensor math on. They aren’t widely available yet, but we have recently launched an alpha program.
The TensorFlow distributed execution engine abstracts away the many supported devices and provides a high performance-core implemented in C++ for the TensorFlow platform.
On top of that sit the Python and C++ frontends (with more to come). The Layers API provides a simpler interface for commonly used layers in deep learning models. On top of that sit higher-level APIs, including Keras (more on the Keras.io site ) and the Estimator API , which makes training and evaluating distributed models easier.
And finally, a number of commonly used models are ready to use out of the box, with more to come.
Machine learning can get complex quickly, and deep learning models can become large. For many model graphs, you need distributed training to be able to iterate within a reasonable time frame. And, you’ll typically want the models you develop to deploy to multiple platforms.
With the current version of TensorFlow, you write code to build a computation graph, then execute it. The graph is a data structure that fully describes the computation you want to perform. This has lots of advantages:
TensorFlow’s high-level APIs, in conjunction with computation graphs, enable a rich and flexible development environment and powerful production capabilities in the same framework.
An upcoming addition to TensorFlow is eager execution, an imperative style for writing TensorFlow. When you enable eager execution, you will be executing TensorFlow kernels immediately, rather than constructing graphs that will be executed later.
Why is this important? Four major reasons:
Once you are satisfied with your TensorFlow code running eagerly, you can convert it to a graph automatically. This will make it easier to save, port, and distribute your graphs.
This interface is in its early (pre-alpha) stages. Follow along on GitHub.
TensorFlow was open sourced in large part to allow the community to improve it with contributions. The TensorFlow team has set up processes to manage pull requests, review and route issues filed, and answer Stack Overflow and mailing list questions.
So far, we’ve had more than 890 external contributors add to the code, with everything from small documentation fixes to large additions like OS X GPU support or the OpenCL implementation. (The broader TensorFlow GitHub organization has had nearly 1,000 unique non-Googler contributors.)
Tensorflow has more than 76,000 stars on GitHub, and the number of other repos that use it is growing every month—as of this writing, there are more than 20,000.
Many of these are community-created tutorials, models, translations, and projects. They can be a great source of examples if you’re getting started on a machine learning task.
Stack Overflow is monitored by the TensorFlow team, and it’s a good way to get questions answered (with 8,000+ answered so far).
The external version of TensorFlow internally is no different than internal, beyond some minor differences.
