# Comparison of ML Frameworks
There are several popular frameworks for DNNs and classical ML. All widely used frameworks are open source. Most but not all support GPU acceleration.
## Deep Learning frameworks
**TensorFlow** (by Google): Offers training, distributed training, and inference (TensorFlow Serving) as well as other capabilities such as TFLite (mobile, embedded), Federated Learning (compute on end-user device, share learnings centrally), TensorFlow\.js, (web-native ML), TFX for platform etc. TensorFlow is widely adopted, especially in enterprise/production-grade ML.
**Keras** (also by Google): A higher-level wrapper around TensorFlow and other frameworks such as R and CNTK, which form the “backend” to Keras in this context.
**PyTorch** (by Facebook): An easy-to-use framework known for rapid prototyping. Facebook recently merged Caffe2 into the PyTorch project to support productionalizing and serving PyTorch-based models. PyTorch is especially popular in the research community.
**Fast.ai** (by Fast.ai team): A library that sits on top of PyTorch to simplify and accelerate deep learning training. Fast.ai is very new and its full reach is not yet known.
**Microsoft Cognitive Toolkit\*** (by Microsoft): A framework focused on large-scale production deployments. The community is small relative to other frameworks.\
\* Formerly **CNTK**
**MXNet** (by Apache but associated with Amazon): An open source deep learning framework focused on large-scale production deployments. MXNet is popular at Microsoft, Intel, and Amazon but not in the research community.
**Gluon** (by Amazon and Microsoft): An attempt to create a Keras-like API layer for MXNet and CNTK. Gluon is not very popular.
**Chainer** (by a Japanese company called Preferred Networks): A deep learning framework that is popular in Japan and supported by tech giants such as IBM, NVIDIA, AWS, and Intel. That said, the Chainer community is relatively small.
**PaddlePaddle** (by Baidu): A scalable deep learning platform originally developed for use on Baidu products that is focused on large-scale production deployments. The PaddlePaddle community is relatively small.
**Deeplearning4j** (by Konduit, related to Eclipse): A deep learning programming library built for companies that need support for Java and Scala. The community is relatively small.
**Caffe** & **Caffe2** (by UC Berkeley): A deep learning framework that is especially suited to image classification and image segmentation. Caffe is not popular anymore but Facebook created a successor called Caffe2 which was recently merged into PyTorch.
## ML frameworks
These frameworks are mostly used for “Classical ML” rather than Deep Learning:
**XGBoost**: An open-source library built for one of the most common machine learning algorithms, [gradient boosting](https://machine-learning.paperspace.com/wiki/gradient-boosting). The community is very large.
**Scikit-learn**: A machine learning library that provides algorithms for many standard machine learning tasks such as clustering, regression, classification, dimensionality reduction, and more. The community is very large.
## Libraries
Other popular machine learning libraries are typically used when preparing data for later use in ML frameworks. These include:
* NumPy (arrays & linear algebra library)
* SciPy (scientific computing library)
* Pandas (data extraction & preparation)
* Matplotlib (plotting & data visualization)
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