Cube Studio is a cloud-native machine learning operations platform that provides end-to-end MLOps capabilities for model development, training, and deployment.
The platform addresses the complexity of managing machine learning workflows by offering integrated tools across the entire ML lifecycle. It enables users to build and orchestrate training pipelines through a drag-and-drop interface, execute distributed training across multiple machines and GPUs, perform hyperparameter search, and deploy inference services with virtual GPU support. The system supports multiple deep learning frameworks including PyTorch, TensorFlow, MXNet, and specialized distributed training libraries like DeepSpeed, Horovod, and Ray. It includes notebook-based online development, automated data labeling capabilities, and support for large language model fine-tuning workflows including supervised fine-tuning, reward modeling, and reinforcement learning training.
Organizations should consider this platform if they need a unified environment for managing ML infrastructure and workflows at scale. It suits teams working with distributed training, large language models, and complex MLOps pipelines who want to avoid assembling multiple point solutions. The platform supports domestic hardware ecosystems including Chinese CPUs, GPUs, and NPUs in the Ascend ecosystem, making it relevant for organizations with localization requirements. It also provides compute rental capabilities and an AI model marketplace, positioning it as a comprehensive platform rather than a single-purpose tool.
The repository has been archived and is no longer maintained, with development activity having concluded. Users requiring ongoing support and updates should migrate to the successor repository that has been established as the continuation of this project.
