TensorFlow™ is an open source software library for high-performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
Machine learning can get complex quickly, and deep learning models can become large. For many model graphs, you need to be 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:
It’s portable, as the graph can be executed immediately or saved to use later, and it can run on multiple platforms: CPUs, GPUs, TPUs, mobile, embedded. Also, it can be deployed to production without having to depend on any of the code that built the graph, only the runtime necessary to execute it.
It’s transformable and optimizable, as the graph can be transformed to produce a more optimal version for a given platform. Also, memory or computer optimizations can be performed and trade-offs made between them. This is useful, for example, in supporting faster mobile inference after training on larger machines.
Support for distributed execution
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.
The TensorFlow team has open sourced a large number of models. You can find them in the tensorflow/models repo. For many of these, the released code includes not only the model graph but also trained model weights. This means that you can try such models out of the box, and you can tune many of them further using a process called transfer learning.
Here are just a few of the recently released models (there are many more):
Among the applications for which TensorFlow is the foundation, are automated image captioning software, such as DeepDream. RankBrain now handles a substantial number of search queries, replacing and supplementing traditional static algorithm-based search results
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