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- Add complete API documentation and architecture guides - Include quick start, installation, and deployment guides - Add troubleshooting and security documentation - Include CLI reference and configuration schema docs - Add production monitoring and operations guides - Implement MkDocs configuration with search functionality - Include comprehensive user and developer documentation Provides complete documentation for users and developers covering all aspects of the FetchML platform.
2.4 KiB
2.4 KiB
User Permissions in Fetch ML
Fetch ML now supports user-based permissions to ensure data scientists can only view and manage their own experiments while administrators retain full control.
Overview
- User Isolation: Each user can only see their own experiments
- Admin Override: Administrators can view and manage all experiments
- Permission-Based: Fine-grained permissions for create, read, update operations
- API Key Authentication: Secure authentication using API keys
Permissions
Job Permissions
jobs:create- Create new experimentsjobs:read- View experiment status and resultsjobs:update- Cancel or modify experiments
User Types
- Administrators: Full access to all experiments and system operations
- Data Scientists: Access to their own experiments only
- Viewers: Read-only access to their own experiments
CLI Usage
View Your Jobs
ml status
Shows only your experiments with user context displayed.
Cancel Your Jobs
ml cancel <job-name>
Only allows canceling your own experiments (unless you're an admin).
Authentication
The CLI automatically authenticates using your API key from ~/.ml/config.toml.
Configuration
API Key Setup
[worker]
api_key = "your-api-key-here"
User Roles
User roles and permissions are configured on the server side by administrators.
Security Features
- API Key Hashing: Keys are hashed before transmission
- User Filtering: Server-side filtering prevents unauthorized access
- Permission Validation: All operations require appropriate permissions
- Audit Logging: All user actions are logged
Examples
Data Scientist Workflow
# Submit your experiment
ml run my-experiment
# Check your experiments (only shows yours)
ml status
# Cancel your own experiment
ml cancel my-experiment
Administrator Workflow
# View all experiments (admin sees everything)
ml status
# Cancel any user's experiment
ml cancel user-experiment
Error Messages
- "Insufficient permissions": You don't have the required permission
- "You can only cancel your own jobs": Ownership restriction
- "Invalid API key": Authentication failed
Migration Notes
- Existing configurations continue to work
- When auth is disabled, all users have admin-like access
- User ownership is automatically assigned to new experiments
For more details, see the architecture documentation.