Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks—such as —that ease the process of acquiring data, training models, serving predictions, and refining future results.

Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. It uses Python to provide a convenient front-end API for building applications with the framework, while executing those applications in high-performance C++.

TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. Best of all, TensorFlow supports production prediction at scale, with the same models used for training.

How TensorFlow works

TensorFlow allows developers to create dataflow graphs—structures that describe how data moves through a , or a series of processing nodes. Each node in the graph represents a mathematical operation, and each connection or edge between nodes is a multidimensional data array, or tensor.

(TPU) silicon for further acceleration. The resulting models created by TensorFlow, though, can be deployed on most any device where they will be used to serve predictions.

TensorFlow benefits

The single biggest benefit TensorFlow provides for machine learning development is abstraction. Instead of dealing with the nitty-gritty details of implementing algorithms, or figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application. TensorFlow takes care of the details behind the scenes.

mode lets you evaluate and modify each graph operation separately and transparently, instead of constructing the entire graph as a single opaque object and evaluating it all at once. The visualization suite lets you inspect and profile the way graphs run by way of an interactive, web-based dashboard.

And of course TensorFlow gains many advantages from the backing of an A-list commercial outfit in Google. Google has not only fueled the rapid pace of development behind the project, but created many significant offerings around TensorFlow that make it easier to deploy and easier to use: the above-mentioned TPU silicon for accelerated performance in Google’s cloud; an for sharing models created with the framework; and incarnations of the framework; and .

One caveat: Some details of TensorFlow’s implementation make it hard to obtain totally deterministic model-training results for some training jobs. Sometimes a model trained on one system will vary slightly from a model trained on another, even when they are fed the exact same data. The reasons for this are slippery—e.g., , ). That said, it is possible to , and TensorFlow’s team is to affect determinism in a workflow.

TensorFlow vs. the competition

TensorFlow competes with a slew of other machine learning frameworks. PyTorch, CNTK, and MXNet are three major frameworks that address many of the same needs. Below I’ve noted where they stand out and come up short against TensorFlow.

  • PyTorch, in addition to being built with Python, and has many other similarities to TensorFlow: hardware-accelerated components under the hood, a highly interactive development model that allows for design-as-you-go work, and many useful components already included. PyTorch is generally a better choice for fast development of projects that need to be up and running in a short time, but TensorFlow wins out for larger projects and more complex workflows.

  • CNTK, the Microsoft Cognitive Toolkit, like TensorFlow uses a graph structure to describe dataflow, but focuses most on creating deep learning neural networks. CNTK handles many neural network jobs faster, and has a broader set of APIs (Python, C++, C#, Java). But CNTK isn’t currently as easy to learn or deploy as TensorFlow.

  • Apache MXNet, adopted by Amazon as the premier deep learning framework on AWS, can scale almost linearly across multiple GPUs and multiple machines. It also supports a broad range of language APIs—Python, C++, Scala, R, JavaScript, Julia, Perl, Go—although its native APIs aren’t as pleasant to work with as TensorFlow’s.