  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. To fill this gap, we take the first step towards understanding how
the most widely-used DL frameworks and platforms support the DL software
development and deployment. We conduct a systematic study on these frameworks
and platforms by using two types of DNN architectures and three popular
datasets. (1) For development process, we investigate the prediction accuracy
under the same runtime training configuration or same model weights/biases. We
also study the adversarial robustness of trained models by leveraging the
existing adversarial attack techniques. The experimental results show that the
computing differences across frameworks could result in an obvious prediction
accuracy decline, which should draw the attention of DL developers. (2) For
deployment process, we investigate the prediction accuracy and performance
(refers to time cost and memory consumption) when the trained models are
migrated/quantized from PC to real mobile devices and web browsers. The DL
platform study unveils that the migration and quantization still suffer from
compatibility and reliability issues. Meanwhile, we find several DL software
bugs by using the results as a benchmark. We further validate the results
through bug confirmation from stakeholders and industrial positive feedback to
highlight the implications of our study. Through our study, we summarize
practical guidelines, identify challenges and pinpoint new research directions.
