get-available-resources

Check hardware resources by running a Python detection script with psutil to collect CPU, memory, disk, and GPU metrics. This generates a structured JSON report to inform execution planning and prevent resource-related workflow failures.

Hardware resource detection is the process of profiling system availability to guide computer science workflows. It collects metrics on CPU, memory, disk, and GPU to generate a report that helps choose appropriate parallelism and memory strategies.

Yes, you need a Python environment with the psutil library to detect system hardware for experiment sweeps. Psutil provides the required cross-platform system monitoring capabilities to collect CPU, memory, and disk metrics for the report.

Generate a resource report by running the detect_resources.py script in your Python environment. The script applies psutil to profile the local machine or cluster nodes, outputting a structured JSON file with hardware availability metrics.

Yes, you can use resource detection to plan parallelism and memory strategy. The generated JSON report details available hardware resources, providing recommendations to choose appropriate data-handling approaches for your workloads.