An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms

Understand

Deep Learning (DL) has recently achieved tremendous success.

• A variety of DL frameworks and platforms play a key role to catalyze such progress.
• However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment.
• Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice.