get-available-resources

You can check available system resources by running automated detection scripts that identify CPU cores, GPU availability, RAM, and disk space. This resource detection helps determine if you have enough memory to load a dataset directly or need out-of-core processing for large data analysis.

GPU detection works across macOS, Linux, and Windows, specifically identifying NVIDIA, AMD, and Apple Silicon GPUs. This cross-platform support ensures you can accurately determine GPU availability for accelerating complex computations regardless of your operating system.

The best way to decide between parallel processing and out-of-core computing is to analyze your detected CPU cores and memory. Strategic recommendations are generated based on available resources, advising memory-efficient libraries like Dask or Zarr when RAM is insufficient to load datasets directly.

You need the psutil dependency installed to detect CPU cores and memory for computational optimization. This library provides the underlying system monitoring capabilities required to report available resources and generate strategic recommendations for parallel processing or GPU acceleration.

You should use memory-efficient libraries like Dask or Zarr instead of loading datasets directly when detected available memory is insufficient for your dataset size. The generated resource recommendations guide this transition to out-of-core computing to prevent memory exhaustion during complex scientific tasks.