get-available-resources

To check available system resources for scientific computing, run the detection script to identify CPU cores, GPU types, RAM, and disk space, outputting the hardware profile in JSON format for programmatic use.

You can get recommendations for parallel processing and GPU acceleration by analyzing your detected CPU and GPU resources, which provides actionable advice on memory-efficient libraries like Dask and optimal data handling strategies.

Yes, you need psutil installed, as it is the required dependency for the Python scripts to successfully query system information and detect your available CPU cores and memory.

Yes, you can use this to confirm GPU availability and VRAM before training machine learning models, as it automatically detects NVIDIA, AMD, or Apple Silicon GPUs to help prevent performance bottlenecks.

The best way to optimize memory management for large datasets is to leverage the strategic recommendations provided after resource detection, which advise using memory-efficient libraries like Dask or Zarr based on your available RAM.

The resource detection script outputs data in JSON format, providing a structured programmatic representation of your system's CPU, GPU, memory, and disk space capabilities for scientific computing optimization.