system-info

System Info

Gather a complete picture of available compute before designing or launching experiments. After collecting the data, produce a structured profile and write it to memory/system-profile.md in the project sandbox.

---

When to Use

• At the start of any project involving training, fine-tuning, or large-scale data processing
• When a training run is unexpectedly slow or crashes (re-run to check resource pressure)
• Before deciding batch size, number of workers, mixed precision settings, or dataset size

---

Step 1 — Detect OS

uname -s

• Linux → use Linux commands below
• Darwin → use macOS commands below
• Windows → use PowerShell commands below (limited support)

---

Step 2 — CPU

Linux:

lscpu | grep -E "Model name|Socket|Core|Thread|MHz"
nproc

macOS:

sysctl -n machdep.cpu.brand_string
sysctl -n hw.physicalcpu hw.logicalcpu

Record: model name, physical cores, logical cores (threads).

---

Step 3 — RAM

Linux:

free -h

macOS:

sysctl -n hw.memsize | awk '{print $1/1024/1024/1024 " GB"}'
vm_stat | grep -E "Pages free|Pages active|Pages inactive|Pages wired"

Record: total RAM, available RAM. On macOS, note that vm_stat pages are 16KB each.

---

Step 4 — Disk

Linux / macOS:

df -h .

Run from within the project sandbox_root to see available space on the relevant partition.

Record: total disk, used, available on the partition where the project lives.

---

Step 5 — GPU Detection

Check for NVIDIA GPU (Linux / Windows WSL)

nvidia-smi --query-gpu=name,memory.total,memory.free,utilization.gpu,driver_version --format=csv,noheader

If nvidia-smi is not found, no NVIDIA GPU is available.

Also check CUDA availability via Python:

python3 -c "import torch; print('CUDA:', torch.cuda.is_available()); print('Device count:', torch.cuda.device_count()); [print(f'  GPU {i}:', torch.cuda.get_device_name(i)) for i in range(torch.cuda.device_count())]"