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With the rapid growth of the Internet of Things (IoT), there is a massive number of constrained devices connected to the internet, resulting in the generation of large data volume. The deep learning (DL) has become a promised solution to extract more valuable knowledge from collected data but struggle to execute DL algorithms due to limited computing resources of IoT devices. Leveraging the computation power of cloud computing, we could offload the data to cloud servers for processing and response results to the devices. But, this paradigm leads to a significant increase in latency and security risks. Therefore, there have been many efforts to perform DL algorithms on edge devices, which are close to the data sources. Deploying various DL systems to such edge devices a complex process due to the diversity of DL models and running environment configuration. In this paper, we propose a light-weight framework …