get-available-resources

System resources like CPU, memory, disk, and GPU can be detected automatically to guide computational planning. A script gathers hardware metrics and outputs a JSON file with recommendations for parallelism, memory allocation, and processing strategies.

You should detect CPU and GPU resources before heavy analyses, model training, or large data processing. This helps determine whether to use in-memory or out-of-core processing, CPU or GPU usage, and appropriate parallelism strategies.

To decide between in-memory and out-of-core processing, assess available memory and disk space first. Resource detection evaluates hardware capacity and generates automated recommendations for data handling strategies based on available memory.

Yes, the system resource detection script requires the psutil Python library to gather CPU, memory, disk, and OS information. This dependency enables accurate hardware detection and automated computational recommendations.

The resource detection output uses a .claude_resources.json file. This structured format contains detected hardware information alongside automated recommendations for parallelism, memory usage, GPU backend selection, and data handling strategies.

Yes, resource detection can guide GPU usage for model training by detecting available GPU backends. The script includes automated recommendations in the output file to help determine whether CPU or GPU usage is appropriate for your specific workload.