get-available-resources

To detect system resources like CPU, GPU, and memory, you can run a Python script that scans your hardware and generates a JSON report. This report identifies available cores, memory, disk space, and GPU backends to guide your computational strategy.

GPU detection works with NVIDIA, AMD, and Apple Silicon hardware. The resource discovery process identifies the specific GPU backend available on your system, allowing you to tailor parallel processing workflows to your machine's capabilities.

Generating recommendations for parallel processing based on system memory involves scanning your hardware resources and producing a structured payload. This payload provides scalable guidance for memory strategies and large-data handling to prevent overcommitment during analysis.

You need the psutil Python library to assess disk space and OS details for scientific tasks. This dependency enables the resource detection script to accurately probe your system environment and output a resource report.

You can load a generated JSON resource report directly into your analysis pipeline. Running the resource detector produces a file that your downstream tooling can consume to automatically adjust computations based on detected hardware.

The best way to avoid overcommitting memory during genomics analysis is to run a resource detection script beforehand. It assesses your system limits and generates memory strategy recommendations to safely scale your data processing.