Project Repository Type Explanation
On GitCode, you can choose the appropriate repository type and visibility settings based on your project's needs to better manage and showcase your project content.
Project Visibility
Based on the usage scenario of the project, you can set the following two types of visibility:
- Public Projects
- All users can access the project content.
- Suitable for open-source projects, tutorial examples, or publicly shared resources.
- Supports community users submitting Issues, participating in discussions, and contributing code.
- Private Projects
- Only project members can access it, ensuring the privacy of the project content.
- Suitable for internal development, sensitive information storage, or personal projects.
- Team members can be invited as needed for collaboration.
Repository Types
GitCode supports three main repository types to help users flexibly store and manage different types of resources:
| Type | Purpose | Supported File Types | Typical Application Scenarios |
|---|---|---|---|
| Code Repository | Used for hosting source code, scripts, and configuration files. | .py, .js, .java, .sh, etc. | Web applications, backend services, automation scripts |
| Model Repository | Designed specifically for machine learning and deep learning models, facilitating the storage and sharing of trained models. | .h5, .onnx, .pt, .pkl | AI model library, pre-trained models, deployment models |
| Dataset Repository | Used for storing and sharing structured or unstructured data, suitable for large-scale data storage and processing. | .csv, .json, .zip, .txt | Research datasets, experimental data, public data sharing |
| Space | Used for building and deploying applications or demonstrating machine learning models, supporting online interaction. | .py, .js, .md, .html | Interactive applications, machine learning demonstrations, web tools |
How to Choose the Repository Type?
- If you need to host and manage source code, choose a Code Repository.
- If your project involves AI/ML models, it is recommended to use a Model Repository.
- If you need to share or process datasets, choose a Dataset Repository.
- If you need to build and deploy applications or demonstrate machine learning models, choose a Space.
Choosing the appropriate project visibility and repository type can help improve team collaboration efficiency and resource management effectiveness.