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Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep learning. Even while several large-scale solutions offer distributed execution environments, open-source alternatives that integrate scalable runtime tools, deep learning and data frameworks on high-performance computing platforms remain crucial for accessibility and flexibility. In this paper, we introduce Deep Radical-Cylon(RC), a heterogeneous runtime system that combines data engineering, deep learning frameworks, and workflow engines across several HPC environments, including cloud and supercomputing infrastructures. Deep RC supports heterogeneous systems with accelerators, allows the usage of communication libraries like \texttt{MPI}, \texttt{GLOO} and \texttt{NCCL} across multi-node setups, and facilitates parallel and distributed deep learning pipelines by utilizing Radical Pilot as a task execution framework. By attaining an end-to-end pipeline including preprocessing, model training, and postprocessing with 11 neural forecasting models (PyTorch) and hydrology models (TensorFlow) under identical resource conditions, the system reduces 3.28 and 75.9 seconds, respectively. The design of Deep RC guarantees the smooth integration of scalable data frameworks, such as Cylon, with deep learning processes, exhibiting strong performance on cloud platforms and scientific HPC systems. By offering a flexible, high-performance solution for resource-intensive applications, this method closes the gap between data preprocessing, model training, and postprocessing. 

Today’s problems require a plethora of analytics tasks to be conducted to tackle state-of-the-art computational challenges posed in society impacting many areas including health care, automotive, banking, natural language processing, image detection, and many more data analytics-related tasks. Sharing existing analytics functions allows reuse and reduces overall effort. However, integrating deployment frameworks in the age of cloud computing are often out of reach for domain experts. Simple frameworks are needed that allow even non-experts to deploy and host services in the cloud. To avoid vendor lock-in, we require a generalized composable analytics service framework that allows users to integrate their services and those offered in clouds, not only by one, but by many cloud compute and service providers.We report on work that we conducted to provide a service integration framework for composing generalized analytics frame-works on multi-cloud providers that we call our Generalized AI Service (GAS) Generator. We demonstrate the framework’s usability by showcasing useful analytics workflows on various cloud providers, including AWS, Azure, and Google, and edge computing IoT devices. The examples are based on Scikit learn so they can be used in educational settings, replicated, and expanded upon. Benchmarks are used to compare the different services and showcase general replicability.