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The major deep learning frameworks today are the following:

Framework Website
TensorFlow www.tensorflow.org
Keras keras.io
CNTK microsoft.com/cognitive-toolkit
MXNet mxnet.apache.org
Caffe2 caffe2.ai
Torch torch.ch
PyTorch pytorch.org
Deeplearning4j deeplearning4j.org
Intel Nervana neon.nervanasys.com

All can be implemented in production on distributed systems, with Deeplearning4j being the only one written natively in java. Keras is a user-friendly frontend to TensorFlow and CNTK. TensorFlow is by far the most popular – one measure of adoption is the number of stars the framework has on github. As of today, those numbers are:

Framework Jul 17 Jun 18 Mar 20
TensorFlow 62,199 103,087 141,493
Keras 17,086 30,726 47,017
CNTK 11,593 14,633 16,694
MXnet 10,217 14,218 18,423
Caffe2 5,076 24,544 archived
Torch 7,038 7,739 8,476
PyTorch 5,647 16,494 36,485
DL4j 6,850 9,149 11,511
Intel Nervana 3,093 3,569 3,828

Table 1 – Framework vs stars on github

As we can see from the table and from Figure 1 below, TensorFlow is by far the most popular framework. Growth is particularly strong in TensorFlow, Keras, Caffe2 and PyTorch.

 

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Figure 1 - The Dramatic Rise of TensorFlow

Putting aside the astounding rise of TensorFlow, all of these frameworks are being used extensively within the companies from which they are primarily being developed, e.g.,

Company Framework
Amazon MXNet
Microsoft CNTK
Google TensorFlow
Facebook PyTorch
Intel Neon

Keras is a frontend and DL4J is used in several enterprises, so they are all getting traction in the Enterprise. The DL4J commercial arm is here https://skymind.ai. Validation that these frameworks are being used in production comes from the fact that they are being used in these hyperscale companies, being applied at scale to some of the largest data sets in the world.

References:
Benchmarking state-of-the-art deep learning software tools, Feb 17, 2017
The Top 10 deep learning frameworks, Packt, May 25, 2017
Google’s latest platform play is artificial intelligence, and it’s already winning, The Verge May 18, 2017