vr-run-experiment

Automate Python experiment execution by specifying an experiment directory containing your scripts and YAML configuration files. The tool automatically runs the code following your predefined plan to ensure consistent, reproducible automated testing and benchmarking.

Ensure reproducibility for data-intensive scientific experiments by executing code through a consistent setup defined by YAML configurations. This approach standardizes the environment and execution plan, automatically logging errors to maintain reliable, repeatable benchmarking results.

Experiment results are collected and organized in a structured directory containing files like raw_results.json and summary_stats.json. This structured collection ensures that outputs from your automated experiment execution are systematically preserved for later analysis.

Yes, you need Python and YAML to run custom experiment automation workflows. Python is required to interpret the custom experiment code, while YAML is processed to read the configuration files that define the execution plan and parameters.

Yes, you can use this for automated testing and benchmarking of technical experiments. It is specifically ideal for data-intensive scientific workloads, executing custom scripts based on a predefined experiment plan and configuration to validate performance.