Spaces
Create and manage spaces for your machine learning projects.
What is a Space?
A space is a dedicated environment for developing, training, and deploying machine learning models.
- Development - Write and test code
- Training - Train models with datasets
- Deployment - Deploy models as endpoints
Creating a Space
Create a new space repository:
Via API:
POST /api/spaces
Authorization: Bearer <your-token>
Content-Type: application/json
{
"name": "sentiment-analysis",
"namespace": "username",
"visibility": "public",
"description": "Space for sentiment analysis project"
}Naming conventions:
- Use descriptive names that indicate the project content
- Include version or purpose when applicable
- Use lowercase with hyphens or underscores
- Example:
sentiment-analysis-v1
Space Features
Explore the features available in a space:
- Code Editor - Integrated development environment
- Terminal - Run commands and scripts
- File Browser - Manage files and directories
- Version Control - Git integration for version management
- Collaboration - Invite team members to work together
Best Practices
- ✅Organize files - Keep your project structure clean and organized
- ✅Use version control - Commit changes regularly to track progress
- ✅Document code - Add comments and documentation for readability
- ✅Test models - Validate model performance before deployment