What is cloud?

PaddlePaddle Cloud is a platform that lets teams run AI and deep-learning training jobs across large clusters of computers instead of a single machine. Think of it as a way to submit a heavy computational task from your laptop and have it automatically distributed across many machines with powerful graphics cards, then monitor it through a web browser or command line. It's designed for organizations that have access to a cluster of servers and want to make that computing power available to their researchers or engineers without everyone needing to manage the underlying hardware. At its core, the platform connects two pieces of technology. It uses PaddlePaddle, an open-source deep-learning framework, to handle the actual AI training. For the infrastructure side, it relies on Kubernetes, a popular system for orchestrating containers across multiple machines. The software itself is built with a mix of JavaScript for the frontend interface and Python (using the Django framework) for the backend. Users interact with a web portal or CLI to submit their jobs, and the platform handles scheduling those jobs onto the available cluster resources, managing data storage, and returning results. The typical user is a company or research lab that has invested in a GPU cluster but needs a clean way to share it among team members. For example, a data science team training a large language model could use this platform to queue up experiments, while an individual researcher could log in to run a specific training job without needing to know which physical machine it ends up running on. The platform supports shared datasets and storage backends like CephFS, so teams can keep common training data in one place on the cluster. Setting it up is not a trivial task and assumes you already have a Kubernetes cluster running. The README walks through deploying the necessary database, configuring network routing, and mounting storage volumes, which requires some infrastructure knowledge. There is also an option to test it locally using a tool called minikube for those who want to try it out before committing to a full deployment. Documentation is currently more complete in Chinese than English, with English tutorials noted as forthcoming.