get-available-resources

You can detect available CPU cores, memory, GPU backends, and disk space by running a resource-detection script that generates a JSON file with system metrics and actionable recommendations for parallel processing.

You need to check system resources for out-of-core computing at project initiation and before compute-intensive tasks to determine if available memory and disk space constraints require memory-efficient strategies.

To get machine-readable recommendations for GPU acceleration and memory strategy, run the detection script to produce a JSON recommendations block that downstream code can consume directly.

Yes, psutil is used as the dependency to detect OS details and resource constraints across CPU, memory, and disk backends, providing actionable guidance for scientific computation workflows.

The best way to guide resource-aware computation before model training is to generate a resource report summarizing available system resources to inform decisions about parallel processing and GPU acceleration.

Yes, the resource detection output exposes a machine-readable recommendations block that downstream code can consume to automatically apply appropriate memory strategies and parallel processing configurations.