Cube-Studio

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Cube Studio is an open-source cloud-native machine learning platform that supports the entire MLOps algorithm pipeline. It offers online notebook development, distributed training, hyperparameter search, and inference service virtualization.

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At a glance

Pricing

Open Source

Free tier

Yes

API

—

Skill level

Technical

About

What is cube-studio?

Cube Studio is an open-source, cloud-native, one-stop platform designed for machine learning, deep learning, and large AI models. It covers the full MLOps algorithm lifecycle, from online notebook development and drag-and-drop task flow pipeline orchestration to multi-machine, multi-card distributed training and hyperparameter search. The platform also provides inference service VGPU virtualization, edge computing, and automated annotation capabilities. It supports fine-tuning and training of large models like DeepSeek, VLLM, Ollama, and Mindie, along with private knowledge bases and an AI model market. Cube Studio is compatible with domestic CPUs/GPUs/NPUs (Ascend ecosystem), RDMA, and various distributed frameworks including PyTorch, TensorFlow, MXNet, DeepSpeed, Paddle, ColossalAI, Horovod, and Ray.

Best used for

Ideal for machine learning engineers and developers who need to streamline the entire MLOps workflow, from data preparation and model training to deployment and monitoring. Especially valuable for teams working with large AI models, distributed computing, and requiring robust infrastructure for AI development.

Common actions

develop machine learning models

orchestrate ML pipelines

deploy AI services

manage model lifecycle

train large language models

deepfakelow-code/no-codecollaborationworkflowsopen-sourceautomated workflowgithub copilot"AI Agents"face swapping

Capabilities

Key features

Cloud-native ML platform
Notebook online development
Drag-and-drop pipeline
Distributed training
Hyperparameter search
Inference service virtualization
Automated annotation

Target Audience

developermachine-learning-engineerdata-scientist

Integrations

pytorchtensorflowmxnetdeepspeedpaddlecolossalaihorovodray+ 19 more

Pricing & Plans

Open Source

Free

FAQs

What types of AI models can be trained and deployed using Cube Studio?

Cube Studio supports a wide range of AI models, including machine learning, deep learning, and large language models (LLMs). It facilitates fine-tuning and training for models like DeepSeek, VLLM, Ollama, and Mindie, covering various tasks from classification to reinforcement learning.

Does Cube Studio support different hardware and distributed computing frameworks?

Yes, Cube Studio offers extensive support for diverse hardware, including CPUs, GPUs (T4, V100, A100, domestic GPUs like Ascend, Haiguang DCU, Huawei NPU), and NPUs. It integrates with distributed frameworks such as PyTorch, TensorFlow, DeepSpeed, Horovod, and Ray, and supports RDMA for high-performance computing.

What MLOps capabilities does Cube Studio provide for workflow management?

Cube Studio provides comprehensive MLOps capabilities, including online notebook development, drag-and-drop pipeline orchestration, distributed training, hyperparameter search, and inference service virtualization. It also features automated annotation, model management, and an AI model market for a complete lifecycle solution.

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Open Source & Models Code Assistants No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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AI Agents & Automation › AI Frameworks & Infra

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