TensorFlow的2.0版本保持了其作为业界领先的机器学习框架的突出地位。TensorFlow最初是一个数字处理程序包，后来逐渐扩展为包括支持各种机器学习方法和执行环境（从移动CPU到大型GPU群集）的库。在此过程中，出现了许多框架，以简化网络创建和训练的任务。同时，其他框架（尤其是PyTorch）提供了一种命令式编程模型，该模型使调试和执行变得越来越容易。TensorFlow 2.0现在默认为命令流（立即执行），并采用Keras作为单个高阶API。尽管这些更改提高了TensorFlow的可用性并使其较PyTorch更具竞争力，但这是一次重大的重写，常常破坏向后兼容性——TensorFlow生态系统中的许多工具和服务框架都无法立即适配新版本。目前，请考虑是否要在TensorFlow 2.0中进行设计和试验，或恢复到版本1以在生产环境中服务和运行模型。
Google's TensorFlow is an open source machine-learning platform that can be used for everything from research through to production and will run on hardware from a mobile CPU all the way to a large GPU compute cluster. It's an important platform because it makes implementing deep-learning algorithms much more accessible and convenient. Despite the hype, though, TensorFlow isn't really anything new algorithmically: All of these techniques have been available in the public domain via academia for some time. It's also important to realize that most businesses are not yet doing even basic predictive analytics and that jumping to deep learning likely won't help make sense of most data sets. For those who do have the right problem and data set, however, TensorFlow is a useful toolkit.
