File size: 3,344 Bytes
3f2dde4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | from __future__ import annotations
from dataclasses import asdict, dataclass
from pathlib import Path
import os
import platform
import shutil
@dataclass(slots=True)
class HardwareSpecs:
hostname: str
platform: str
cpu_model: str
physical_cores: int
logical_cores: int
memory_total_gb: float
memory_available_gb: float
disk_total_gb: float
disk_free_gb: float
python_version: str
cuda_available: bool
cuda_device: str
def to_dict(self) -> dict[str, object]:
return asdict(self)
def collect_hardware_specs() -> HardwareSpecs:
try:
import psutil
except Exception: # pragma: no cover - psutil is expected to be present in the venv
psutil = None
hostname = platform.node() or os.environ.get("COMPUTERNAME", "unknown")
platform_name = f"{platform.system()} {platform.release()}"
cpu_model = platform.processor() or platform.machine() or "unknown"
physical_cores = psutil.cpu_count(logical=False) if psutil else 0
logical_cores = psutil.cpu_count(logical=True) if psutil else os.cpu_count() or 0
memory_total_gb = 0.0
memory_available_gb = 0.0
disk_total_gb = 0.0
disk_free_gb = 0.0
if psutil:
memory = psutil.virtual_memory()
memory_total_gb = memory.total / (1024**3)
memory_available_gb = memory.available / (1024**3)
disk = psutil.disk_usage(str(Path.home().anchor or Path.cwd().anchor or Path.cwd()))
disk_total_gb = disk.total / (1024**3)
disk_free_gb = disk.free / (1024**3)
cuda_available = False
cuda_device = "cpu"
try:
import torch
cuda_available = bool(torch.cuda.is_available())
if cuda_available:
cuda_device = torch.cuda.get_device_name(0)
except Exception:
pass
return HardwareSpecs(
hostname=hostname,
platform=platform_name,
cpu_model=cpu_model,
physical_cores=int(physical_cores or 0),
logical_cores=int(logical_cores or 0),
memory_total_gb=memory_total_gb,
memory_available_gb=memory_available_gb,
disk_total_gb=disk_total_gb,
disk_free_gb=disk_free_gb,
python_version=platform.python_version(),
cuda_available=cuda_available,
cuda_device=cuda_device,
)
def hardware_table_rows(specs: HardwareSpecs) -> list[dict[str, str]]:
return [
{"Metric": "Hostname", "Value": specs.hostname},
{"Metric": "Platform", "Value": specs.platform},
{"Metric": "CPU", "Value": specs.cpu_model},
{"Metric": "Physical Cores", "Value": str(specs.physical_cores)},
{"Metric": "Logical Cores", "Value": str(specs.logical_cores)},
{"Metric": "Memory Total (GB)", "Value": f"{specs.memory_total_gb:.2f}"},
{"Metric": "Memory Available (GB)", "Value": f"{specs.memory_available_gb:.2f}"},
{"Metric": "Disk Total (GB)", "Value": f"{specs.disk_total_gb:.2f}"},
{"Metric": "Disk Free (GB)", "Value": f"{specs.disk_free_gb:.2f}"},
{"Metric": "Python", "Value": specs.python_version},
{"Metric": "CUDA", "Value": "yes" if specs.cuda_available else "no"},
{"Metric": "CUDA Device", "Value": specs.cuda_device},
]
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