Goal: Create a new plugin for integrating Jenkins with one of Machine Learning tools (e.g. Jupyter Python, TensorBoard, or Sacred)
The main goal of this project is integrating Machine Learning workflow including Data preprocessing, Model Training, Evaluation and Prediction with Jenkins build tasks. This plugin will be capable of executing code fragments via IPython kernel as currently supported by Jupyter Notebook. Version control will be handled as an added advantage of this project.
An IPython plugin with pipeline compatible
Improved config.jelly for the plugin
File parsers for ipynb, py and text(containing python code) files
Code segment extractor
Implement a code editor
Output console with static visual components
The community bonding period is all about, well, community bonding. Rather than jumping straight into coding, I’ve got some time to learn about Jenkins’s processes - release and otherwise - developer interactions, codes of conduct, etc.
Implement the configuration part of Ipython kernel interpreter
Implement file parser
Improve current config.jelly
Improve form validations
Increase the test coverage and documentation
Implement code editor
Extend the editor for test purposes with pre-tested codelets
Testing and bug fixing
Improve the documentation
Discuss with mentors about phase 3 coding
Design and implement code segment extractor
Implement JSON generator for the results
Integrate whole previous work and testing
Git integration for code segments (optional)
User documentation and examples
Writing developer documentation
Git integration on plugin if not implemented during coding phase.
Implementing Interactive visualizer for data sets and results.
Community discussions and design to integrate other kernels with Jenkins