Chapter 1 Introduction Github Automation For Scientists This course covers how to use github actions for scientific software development. we encourage the recognition that scientific software can take many forms that can all benefit from the concepts of continuous integration and continuous deployment. This course covers how to use github actions for scientific software development. we encourage the recognition that scientific software can take many forms that can all benefit from the concepts of continuous integration and continuous deployment.
Chapter 1 Introduction Github Automation For Scientists This course covers how to use github actions for scientific software development. we encourage the recognition that scientific software can take many forms that can all benefit from the concepts of continuous integration and continuous deployment. The introductory video for the github automation for scientists course: hutchdatascience.org github automation for scientists introduction. Github actions can be used to do automated tests on your code, data pipeline validation, or machine learning model comparisons. this maintains the quality of code and the consistency of model performance prior to the fresh features being combined into production. Through a sequence of examples, we will demonstrate some of github actions’ applications to scientific workflows, such as scheduled deployment of algorithms to sensor streams, updating visualizations based on new data, processing large datasets, model versioning and performance benchmarking.
Chapter 1 Introduction Github Automation For Scientists Github actions can be used to do automated tests on your code, data pipeline validation, or machine learning model comparisons. this maintains the quality of code and the consistency of model performance prior to the fresh features being combined into production. Through a sequence of examples, we will demonstrate some of github actions’ applications to scientific workflows, such as scheduled deployment of algorithms to sensor streams, updating visualizations based on new data, processing large datasets, model versioning and performance benchmarking. Through a sequence of examples, we will demonstrate some of github actions’ applications to scientific workflows, such as scheduled deployment of algorithms to sensor streams, updating visualizations based on new data, processing large datasets, model versioning and performance benchmarking. The course is intended for individuals in the biomedical sciences who wish to make their work more reproducible through the use of automation. it focuses on the basics of continuous integration continuous deployment techniques using the github actions software. Hands on introduction to github actions for data scientists learn how to automate experiment tracking with weights & biases, unit testing, artifact creation, and lots more…. Learn the fundamentals of git and github to better understand how github actions integrates with your workflow. work through online tutorials that demonstrate how to set up and configure github actions for common scientific workflows.
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