get-available-resources

System resource detection scripts identify CPU cores, GPU availability, RAM, and disk space, outputting a JSON report. This report provides actionable recommendations on whether to use parallel processing, GPU acceleration, or out-of-core computing for your specific hardware.

You should use out-of-core computing for large datasets when system resource detection reveals insufficient RAM for in-memory processing. By evaluating available memory and disk space, you can determine if frameworks like Dask are needed to handle data that exceeds physical memory limits.

Yes, resource detection supports NVIDIA, AMD, and Apple Silicon GPUs. It automatically identifies available graphics hardware to provide strategic recommendations on whether GPU acceleration is viable for your specific computational workload.

Run a system resource check to evaluate available RAM and disk space against your dataset size. This identifies whether your machine has sufficient memory for in-memory analysis or if you should adopt memory-efficient strategies and out-of-core processing for large datasets.

Yes, psutil is a required dependency to detect system resources. The psutil Python library enables the script to accurately read hardware metrics like CPU cores, memory, and disk space, which are used to generate strategic computational recommendations.

Run a system resource evaluation to decide between parallel processing and GPU acceleration. By detecting available CPU cores and GPU hardware, the tool provides strategic recommendations tailored to your system's capabilities for optimal computational performance.