502 lines
15 KiB
Python
502 lines
15 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Generate a LaTeX longtable with system + software info for a thesis (Linux + NVIDIA).
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Requirements (preflight will check and error if missing):
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- Linux OS
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- lscpu (util-linux)
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- Python packages: nvidia-ml-py3 (pynvml), torch, numpy, scipy, scikit-learn
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- NVIDIA driver present and at least one GPU visible via NVML
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What it reports (per user’s list):
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System:
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- OS name + version + distribution (Linux) + kernel version + system arch
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- CPU model name, number of cores and threads, base frequencies (best-effort via lscpu)
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- Total RAM capacity
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- GPU(s): model name (only the newer one; prefer a name matching “4090”, else highest compute capability),
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memory size, driver version, CUDA (driver) version, cuDNN version (if used via PyTorch)
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Software environment:
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- Python version
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- PyTorch version + built CUDA/cuDNN version
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- scikit-learn version
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- NumPy / SciPy version (+ NumPy build config summary: MKL/OpenBLAS/etc.)
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"""
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import argparse
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import os
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import platform
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import re
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import shutil
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import subprocess
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import sys
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from typing import Dict, List, Tuple
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# -------------------- Helper --------------------
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def _import_nvml():
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"""
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Try to import NVML from the supported packages:
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- 'nvidia-ml-py' (preferred, maintained): provides module 'pynvml'
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- legacy 'pynvml' (deprecated but still widely installed)
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Returns the imported module object (with nvml... symbols).
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"""
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try:
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import pynvml as _nvml # provided by 'nvidia-ml-py' or old 'pynvml'
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return _nvml
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except Exception as e:
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raise ImportError(
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"NVML not importable. Please install the maintained package:\n"
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" pip install nvidia-ml-py\n"
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"(and uninstall deprecated ones: pip uninstall nvidia-ml-py3 pynvml)"
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) from e
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def _to_text(x) -> str:
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"""Return a clean str whether NVML gives bytes or str."""
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if isinstance(x, bytes):
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try:
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return x.decode(errors="ignore")
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except Exception:
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return x.decode("utf-8", "ignore")
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return str(x)
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# -------------------- Utilities --------------------
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def which(cmd: str) -> str:
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return shutil.which(cmd) or ""
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def run(cmd: List[str], timeout: int = 6) -> str:
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try:
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out = subprocess.check_output(cmd, stderr=subprocess.STDOUT, timeout=timeout)
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return out.decode(errors="ignore").strip()
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except Exception:
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return ""
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def human_bytes(nbytes: int) -> str:
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try:
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n = float(nbytes)
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except Exception:
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return ""
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units = ["B", "KiB", "MiB", "GiB", "TiB"]
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i = 0
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while n >= 1024 and i < len(units) - 1:
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n /= 1024.0
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i += 1
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return f"{n:.2f} {units[i]}"
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LATEX_SPECIALS = {
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"\\": r"\textbackslash{}",
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"&": r"\&",
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"%": r"\%",
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"$": r"\$",
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"#": r"\#",
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"_": r"\_",
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"{": r"\{",
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"}": r"\}",
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"~": r"\textasciitilde{}",
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"^": r"\textasciicircum{}",
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}
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def tex_escape(s: str) -> str:
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if s is None:
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return ""
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return "".join(LATEX_SPECIALS.get(ch, ch) for ch in str(s))
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def latex_table(sections: List[Tuple[str, Dict[str, str]]], caption: str) -> str:
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lines = []
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lines.append(r"\begin{table}[p]") # float; use [p] or [tbp] as you prefer
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lines.append(r"\centering")
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lines.append(r"\caption{" + tex_escape(caption) + r"} \label{tab:system_setup}")
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lines.append(r"\begin{tabular}{p{0.34\linewidth} p{0.62\linewidth}}")
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lines.append(r"\toprule")
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lines.append(r"\textbf{Item} & \textbf{Details} \\")
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lines.append(r"\midrule")
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for title, kv in sections:
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if not kv:
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continue
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lines.append(r"\multicolumn{2}{l}{\textbf{" + tex_escape(title) + r"}} \\")
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for k, v in kv.items():
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val = tex_escape(v)
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if "\n" in v or len(v) > 120:
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val = (
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r"\begin{minipage}[t]{\linewidth}\ttfamily\small "
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+ tex_escape(v)
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+ r"\end{minipage}"
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)
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else:
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val = r"\ttfamily " + val
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lines.append(tex_escape(k) + " & " + val + r" \\")
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lines.append(r"\addlinespace")
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lines.append(r"\bottomrule")
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lines.append(r"\end{tabular}")
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lines.append(r"\end{table}")
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preamble_hint = r"""
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% ---- Add to your LaTeX preamble ----
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% \usepackage{booktabs}
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% \usepackage{array}
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% ------------------------------------
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"""
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return preamble_hint + "\n".join(lines)
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def latex_longtable(sections: List[Tuple[str, Dict[str, str]]], caption: str) -> str:
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lines = []
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lines.append(r"\begin{longtable}{p{0.34\linewidth} p{0.62\linewidth}}")
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lines.append(r"\caption{" + tex_escape(caption) + r"} \label{tab:system_setup}\\")
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lines.append(r"\toprule")
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lines.append(r"\textbf{Item} & \textbf{Details} \\")
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lines.append(r"\midrule")
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lines.append(r"\endfirsthead")
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lines.append(r"\toprule \textbf{Item} & \textbf{Details} \\ \midrule")
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lines.append(r"\endhead")
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lines.append(r"\bottomrule")
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lines.append(r"\endfoot")
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lines.append(r"\bottomrule")
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lines.append(r"\endlastfoot")
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for title, kv in sections:
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if not kv:
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continue
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lines.append(r"\multicolumn{2}{l}{\textbf{" + tex_escape(title) + r"}} \\")
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for k, v in kv.items():
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val = tex_escape(v)
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if "\n" in v or len(v) > 120:
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val = (
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r"\begin{minipage}[t]{\linewidth}\ttfamily\small "
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+ tex_escape(v)
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+ r"\end{minipage}"
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)
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else:
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val = r"\ttfamily " + val
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lines.append(tex_escape(k) + " & " + val + r" \\")
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lines.append(r"\addlinespace")
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lines.append(r"\end{longtable}")
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preamble_hint = r"""
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% ---- Add to your LaTeX preamble ----
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% \usepackage{booktabs}
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% \usepackage{longtable}
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% \usepackage{array}
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% ------------------------------------
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"""
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return preamble_hint + "\n".join(lines)
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# -------------------- Preflight --------------------
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REQUIRED_CMDS = ["lscpu"]
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REQUIRED_MODULES = [
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"torch",
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"numpy",
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"scipy",
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"sklearn",
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"pynvml",
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] # provided by nvidia-ml-py
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def preflight() -> List[str]:
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errors = []
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if platform.system().lower() != "linux":
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errors.append(
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f"This script supports Linux only (detected: {platform.system()})."
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)
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for c in ["lscpu"]:
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if not which(c):
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errors.append(f"Missing required command: {c}")
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for m in REQUIRED_MODULES:
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try:
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__import__(m)
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except Exception:
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errors.append(f"Missing required Python package: {m}")
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# NVML driver availability
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if "pynvml" not in errors:
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try:
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pynvml = _import_nvml()
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pynvml.nvmlInit()
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count = pynvml.nvmlDeviceGetCount()
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if count < 1:
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errors.append("No NVIDIA GPUs detected by NVML.")
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pynvml.nvmlShutdown()
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except Exception as e:
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errors.append(f"NVIDIA NVML not available / driver not loaded: {e}")
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return errors
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# -------------------- Collectors --------------------
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def collect_system() -> Dict[str, str]:
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info: Dict[str, str] = {}
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# OS / distro / kernel / arch
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os_pretty = ""
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try:
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with open("/etc/os-release", "r") as f:
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txt = f.read()
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m = re.search(r'^PRETTY_NAME="?(.*?)"?$', txt, flags=re.M)
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if m:
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os_pretty = m.group(1)
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except Exception:
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pass
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info["Operating System"] = os_pretty or f"{platform.system()} {platform.release()}"
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info["Kernel"] = platform.release()
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info["Architecture"] = platform.machine()
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# CPU (via lscpu)
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lscpu = run(["lscpu"])
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def kvs(text: str) -> Dict[str, str]:
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out = {}
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for line in text.splitlines():
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if ":" in line:
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k, v = line.split(":", 1)
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out[k.strip()] = v.strip()
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return out
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d = kvs(lscpu)
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info["CPU Model"] = d.get("Model name", d.get("Model Name", ""))
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# cores / threads
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sockets = d.get("Socket(s)", "")
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cores_per_socket = d.get("Core(s) per socket", "")
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threads_total = d.get("CPU(s)", "")
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if sockets and cores_per_socket:
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info["CPU Cores (physical)"] = f"{cores_per_socket} × {sockets}"
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else:
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info["CPU Cores (physical)"] = cores_per_socket or ""
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info["CPU Threads (logical)"] = threads_total or str(os.cpu_count() or "")
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# base / max freq
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# Prefer "CPU max MHz" and "CPU min MHz"; lscpu sometimes exposes "CPU MHz" (current)
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base = d.get("CPU min MHz", "")
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maxf = d.get("CPU max MHz", "")
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if base:
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info["CPU Base Frequency"] = f"{float(base):.0f} MHz"
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elif "@" in info["CPU Model"]:
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# fallback: parse from model string like "Intel(R) ... @ 2.30GHz"
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m = re.search(r"@\s*([\d.]+)\s*([GM]Hz)", info["CPU Model"])
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if m:
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info["CPU Base Frequency"] = f"{m.group(1)} {m.group(2)}"
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else:
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cur = d.get("CPU MHz", "")
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if cur:
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info["CPU (Current) Frequency"] = f"{float(cur):.0f} MHz"
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if maxf:
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info["CPU Max Frequency"] = f"{float(maxf):.0f} MHz"
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# RAM total (/proc/meminfo)
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try:
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meminfo = open("/proc/meminfo").read()
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m = re.search(r"^MemTotal:\s+(\d+)\s+kB", meminfo, flags=re.M)
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if m:
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total_bytes = int(m.group(1)) * 1024
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info["Total RAM"] = human_bytes(total_bytes)
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except Exception:
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pass
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return info
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def collect_gpu() -> Dict[str, str]:
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"""
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Use NVML to enumerate GPUs and select the 'newer' one:
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1) Prefer a device whose name matches /4090/i
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2) Else highest CUDA compute capability (major, minor), tiebreaker by total memory
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Also reports driver version and CUDA driver version.
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"""
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pynvml = _import_nvml()
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pynvml.nvmlInit()
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try:
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count = pynvml.nvmlDeviceGetCount()
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if count < 1:
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return {"Error": "No NVIDIA GPUs detected by NVML."}
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devices = []
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for i in range(count):
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h = pynvml.nvmlDeviceGetHandleByIndex(i)
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# name can be bytes or str depending on wheel; normalize
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raw_name = pynvml.nvmlDeviceGetName(h)
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name = _to_text(raw_name)
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mem_info = pynvml.nvmlDeviceGetMemoryInfo(h)
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total_mem = getattr(mem_info, "total", 0)
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# compute capability may not exist on very old drivers
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try:
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maj, minr = pynvml.nvmlDeviceGetCudaComputeCapability(h)
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except Exception:
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maj, minr = (0, 0)
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devices.append(
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{
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"index": i,
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"handle": h,
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"name": name,
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"mem": total_mem,
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"cc": (maj, minr),
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# Prefer explicit "4090"
|
|||
|
|
pick = next(
|
|||
|
|
(d for d in devices if re.search(r"4090", d["name"], flags=re.I)), None
|
|||
|
|
)
|
|||
|
|
if pick is None:
|
|||
|
|
# Highest compute capability, then largest memory
|
|||
|
|
devices.sort(key=lambda x: (x["cc"][0], x["cc"][1], x["mem"]), reverse=True)
|
|||
|
|
pick = devices[0]
|
|||
|
|
|
|||
|
|
# Driver version and CUDA driver version can be bytes or str
|
|||
|
|
drv_raw = pynvml.nvmlSystemGetDriverVersion()
|
|||
|
|
drv = _to_text(drv_raw)
|
|||
|
|
|
|||
|
|
# CUDA driver version (integer like 12040 -> 12.4)
|
|||
|
|
cuda_drv_ver = ""
|
|||
|
|
try:
|
|||
|
|
v = pynvml.nvmlSystemGetCudaDriverVersion_v2()
|
|||
|
|
except Exception:
|
|||
|
|
v = pynvml.nvmlSystemGetCudaDriverVersion()
|
|||
|
|
try:
|
|||
|
|
major = v // 1000
|
|||
|
|
minor = (v % 1000) // 10
|
|||
|
|
patch = v % 10
|
|||
|
|
cuda_drv_ver = f"{major}.{minor}.{patch}" if patch else f"{major}.{minor}"
|
|||
|
|
except Exception:
|
|||
|
|
cuda_drv_ver = ""
|
|||
|
|
|
|||
|
|
gpu_info = {
|
|||
|
|
"Selected GPU Name": pick["name"],
|
|||
|
|
"Selected GPU Memory": human_bytes(pick["mem"]),
|
|||
|
|
"Selected GPU Compute Capability": f"{pick['cc'][0]}.{pick['cc'][1]}",
|
|||
|
|
"NVIDIA Driver Version": drv,
|
|||
|
|
"CUDA (Driver) Version": cuda_drv_ver,
|
|||
|
|
}
|
|||
|
|
return gpu_info
|
|||
|
|
finally:
|
|||
|
|
pynvml.nvmlShutdown()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def summarize_numpy_build_config() -> str:
|
|||
|
|
"""
|
|||
|
|
Capture numpy.__config__.show() and try to extract the BLAS/LAPACK backend line(s).
|
|||
|
|
"""
|
|||
|
|
import numpy as np
|
|||
|
|
from io import StringIO
|
|||
|
|
import sys as _sys
|
|||
|
|
|
|||
|
|
buf = StringIO()
|
|||
|
|
_stdout = _sys.stdout
|
|||
|
|
try:
|
|||
|
|
_sys.stdout = buf
|
|||
|
|
np.__config__.show()
|
|||
|
|
finally:
|
|||
|
|
_sys.stdout = _stdout
|
|||
|
|
txt = buf.getvalue()
|
|||
|
|
|
|||
|
|
# Heuristic: capture lines mentioning MKL, OpenBLAS, BLIS, LAPACK
|
|||
|
|
lines = [
|
|||
|
|
l
|
|||
|
|
for l in txt.splitlines()
|
|||
|
|
if re.search(r"(MKL|OpenBLAS|BLAS|LAPACK|BLIS)", l, re.I)
|
|||
|
|
]
|
|||
|
|
if not lines:
|
|||
|
|
# fall back to first ~12 lines
|
|||
|
|
lines = txt.splitlines()[:12]
|
|||
|
|
# Keep it compact
|
|||
|
|
return "\n".join(lines[:20]).strip()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def collect_software() -> Dict[str, str]:
|
|||
|
|
info: Dict[str, str] = {}
|
|||
|
|
import sys as _sys
|
|||
|
|
import torch
|
|||
|
|
import numpy as _np
|
|||
|
|
import scipy as _sp
|
|||
|
|
import sklearn as _sk
|
|||
|
|
|
|||
|
|
info["Python"] = _sys.version.split()[0]
|
|||
|
|
|
|||
|
|
# PyTorch + built CUDA/cuDNN + visible GPUs
|
|||
|
|
info["PyTorch"] = torch.__version__
|
|||
|
|
info["PyTorch Built CUDA"] = getattr(torch.version, "cuda", "") or ""
|
|||
|
|
try:
|
|||
|
|
cudnn_build = torch.backends.cudnn.version() # integer
|
|||
|
|
info["cuDNN (PyTorch build)"] = str(cudnn_build) if cudnn_build else ""
|
|||
|
|
except Exception:
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
# scikit-learn
|
|||
|
|
info["scikit-learn"] = _sk.__version__
|
|||
|
|
|
|||
|
|
# NumPy / SciPy + build config
|
|||
|
|
info["NumPy"] = _np.__version__
|
|||
|
|
info["SciPy"] = _sp.__version__
|
|||
|
|
info["NumPy Build Config"] = summarize_numpy_build_config()
|
|||
|
|
|
|||
|
|
return info
|
|||
|
|
|
|||
|
|
|
|||
|
|
# -------------------- Main --------------------
|
|||
|
|
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
ap = argparse.ArgumentParser(
|
|||
|
|
description="Generate LaTeX table of system/software environment for thesis (Linux + NVIDIA)."
|
|||
|
|
)
|
|||
|
|
ap.add_argument(
|
|||
|
|
"--output", "-o", type=str, help="Write LaTeX to this file instead of stdout."
|
|||
|
|
)
|
|||
|
|
ap.add_argument(
|
|||
|
|
"--caption", type=str, default="Computational Environment (Hardware & Software)"
|
|||
|
|
)
|
|||
|
|
args = ap.parse_args()
|
|||
|
|
|
|||
|
|
errs = preflight()
|
|||
|
|
if errs:
|
|||
|
|
msg = (
|
|||
|
|
"Preflight check failed:\n- "
|
|||
|
|
+ "\n- ".join(errs)
|
|||
|
|
+ "\n"
|
|||
|
|
+ "Please install missing components and re-run."
|
|||
|
|
)
|
|||
|
|
print(msg, file=sys.stderr)
|
|||
|
|
sys.exit(1)
|
|||
|
|
|
|||
|
|
sections: List[Tuple[str, Dict[str, str]]] = []
|
|||
|
|
sections.append(("System", collect_system()))
|
|||
|
|
sections.append(("GPU (Selected Newer Device)", collect_gpu()))
|
|||
|
|
sections.append(("Software Environment", collect_software()))
|
|||
|
|
|
|||
|
|
latex = latex_table(sections, caption=args.caption)
|
|||
|
|
|
|||
|
|
if args.output:
|
|||
|
|
with open(args.output, "w", encoding="utf-8") as f:
|
|||
|
|
f.write(latex)
|
|||
|
|
print(f"Wrote LaTeX to: {args.output}")
|
|||
|
|
else:
|
|||
|
|
print(latex)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|