What are Projects?
Projects are cloud compute instances that run Jupyter kernels for executing notebook code. Each project has its own compute resources, file storage, and runtime environment.Key Concepts
- Compute Type: CPU/memory configuration and hourly cost
- Status: ready, active, or stopped
- Kernel: Python runtime for executing notebook cells
- Token: Secure authentication for Jupyter API access
Project Features
Jupyter Kernels
Execute Python code from notebooks
Terminal Access
Shell access to install packages and run commands
Web App Hosting
Deploy web applications at your-project.runalph.dev
AI Chat
Project-scoped AI conversations for code assistance
Compute Types
Projects use different compute configurations based on your needs:| Type | Resources | Use Case |
|---|---|---|
| Micro | 0.5 CPU, 1GB RAM | Light tasks |
| Small | 2 CPU, 4GB RAM | Data analysis |
| GPU | 4 CPU, 16GB RAM, T4 GPU | Deep learning |
Project Components
IDE
Code editor and chat interface for AI-assisted development.Kernels
Manage Jupyter kernels for executing notebook code.Terminals
Access shell terminals for package installation and debugging.Application
Web apps running in your project accessible via runalph.dev subdomain.Settings
Configure environment variables, compute type, and project details.How Projects Work
- Created in organization: Projects belong to an organization
- Docker-based: Each project runs in an isolated container
- Token-secured: Access controlled via encrypted vault tokens
- Usage tracked: Compute hours and costs monitored in billing
Projects vs Notebooks
- Notebooks: Store code, markdown, and outputs
- Projects: Provide compute to execute notebook code