Note: dstone is in early stages of development and actively evolving. Features and APIs are subject to change.
dstone is a personal project aimed at streamlining the workflow for machine learning researchers and practitioners. It's designed to provide a unified, extensible toolkit that simplifies environment setup, accelerates prototyping, and offers a consistent API across various computing platforms.
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Rapid Development Environment Setup: Utilize Ansible to provide an easy and fast way to set up development environments for ML projects.
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Swift Prototyping: Enable researchers to quickly prototype and visualize results of their ML experiments.
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Cross-Platform Compatibility: Support seamless operation across HPC systems, cloud computing platforms, and local workstations.
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Extensible Plugin System: Allow for easy addition of custom functionality through a flexible plugin architecture.
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Interactive Visualization: Incorporate a Dash-based UI for interactive data exploration and result visualization.
dstone is currently in its early stages of development. I am actively working on:
- Developing Ansible playbooks for environment setup
- Creating the foundation for the Dash-based UI
- Defining the core API structure
- Implementing the basic plugin system
As the project is rapidly evolving, I have not yet finalized installation instructions or usage guidelines. These will be provided as the project matures.
- Comprehensive documentation using Sphinx
- Extensive test suite for ensuring reliability
- Integration with popular ML frameworks and tools
- Community-driven plugin ecosystem
I welcome ideas, suggestions, and contributions! As the project is in its early stages, the best way to contribute is through discussion and idea sharing. Please feel free to open an issue on the GitHub repository to start a conversation.