Projects: Organize and Manage Your ML Work

Projects are the foundation of your machine learning workflow in Trainwave. They provide a centralized hub to organize your training jobs, manage resources, collaborate with your team, and track your progress.

Key Benefits of Projects:

• Organization: Group related training jobs, datasets, and experiments for better clarity and management.
• Collaboration: Share projects with your team members to foster collaboration and knowledge sharing.
• Resource Management: Allocate compute resources and manage secrets at the project level.
• Cost Tracking: Monitor spending and resource usage on a per-project basis (coming soon!).

Project-Level Configuration:

Trainwave allows you to configure secrets and variables at the project level. These secrets and variables are then accessible to all jobs within that project, eliminating the need to hardcode sensitive information in your training scripts. This promotes security and simplifies configuration management.

Team Collaboration:

Projects facilitate seamless collaboration. By inviting team members to your organization, you can grant them access to specific projects, enabling them to contribute to training jobs, share insights, and work together towards common goals.

Create a New Project:

Creating a new project is simple:

1. Go to the Trainwave dashboard: https://trainwave.ai/projects
2. Click the New Project button.
3. Provide a name for your project.
4. Click Create to finalize the process.
5. You can now add it to your job configuration.

Enhanced Project Management (Coming Soon!):

We’re actively working on adding powerful project management features to Trainwave, including:

• Spending controls: Set budget limits and receive alerts to manage costs effectively.
• Project-level metrics: Track key performance indicators and resource usage for each project.
• Granular access control: Fine-grained permissions to control access to project resources.

These upcoming features will provide you with even greater control and insight into your machine learning projects.