Should I use TensorFlow

TL;DR

This paper evaluates TensorFlow's suitability for machine learning practitioners by analyzing its features, comparing implementations, and contrasting it with other frameworks in terms of flexibility, deployment, and performance.

Contribution

It provides a comprehensive assessment of TensorFlow's capabilities and compares it to other frameworks, aiding users in decision-making.

Findings

TensorFlow offers high flexibility and extensibility for research.

Compared to pure Python, TensorFlow improves performance in linear classification.

TensorFlow's deployment capabilities are competitive with other frameworks.

Abstract

Google's Machine Learning framework TensorFlow was open-sourced in November 2015 [1] and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively. This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast TensorFlow to other popular frameworks with respect to modeling capability, deployment and performance and give a brief description of the current adaption of the framework.