DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/.

深度学习正在改变生物图像的分析，但将这些模型应用于大型数据集仍然具有挑战性。在此我们描述了DeepCell Kiosk，这是一种原生云软件，它能动态扩展深度学习工作流程以适应大型成像数据集。为了证明该软件的可扩展性和经济性，我们在约5.5小时内以约250美元的成本在106张100万像素的图像中识别出了细胞核，根据集群配置不同，成本可降至100美元以下。DeepCell Kiosk可在https://github.com/vanvalenlab/kiosk - console下载；在https://deepcell.org/可获取持久部署版本。