Module neuralops.benchmarks.gpu_live_probe
gpu_live_probe.py Executed on the GPU server to return real-time GPU telemetry, active running process, live training loss trajectory, and log streams in JSON format.
Functions
def get_active_process_and_logs()-
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def get_active_process_and_logs(): active_module = None active_model = None queue_logs = "" try: cmd = 'powershell -Command "(Get-CimInstance Win32_Process -Filter \\"name = \'python.exe\'\\").CommandLine"' res = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=4) if res.returncode == 0 and res.stdout: for line in res.stdout.splitlines(): if "run_quality_sweep.py" in line: m = re.search(r"--modules\s+[^,]+,([a-zA-Z0-9_]+)", line) if m: active_module = m.group(1) mm = re.search(r"--model_family\s+([a-zA-Z0-9_]+)", line) if mm: active_model = mm.group(1) elif "run_all_4_" in line and not active_module: m = re.search(r"run_all_4_([a-zA-Z0-9_]+)\.py", line) if m: active_module = m.group(1) except Exception: pass try: for qf in [ "D:/SVD_Project/queue_launcher.log", "D:/SVD_Project/gpu_queue.log", "D:/SVD_Project/results/queue_runner.log", ]: if os.path.exists(qf): with open(qf, "r", encoding="utf-8", errors="ignore") as f: lines = [ l.strip() for l in f.readlines()[-30:] if l.strip() and not l.startswith("**") ] if lines: queue_logs = "\n".join(lines) break except Exception: pass return active_module or "cur_linear", active_model or "qwen3_5", queue_logs def get_live_progress_and_trajectory(target_module=None, target_model=None)-
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def get_live_progress_and_trajectory(target_module=None, target_model=None): results_dir = "D:/SVD_Project/results" if not os.path.exists(results_dir): return None candidate_files = [] if target_module: candidate_files.extend(glob.glob(os.path.join(results_dir, f"bench_{target_module}.log"))) candidate_files.extend( glob.glob( os.path.join(results_dir, f"unified_{target_module}", "**", "console.log"), recursive=True, ) ) candidate_files.extend(glob.glob(os.path.join(results_dir, "bench_*.log"))) candidate_files.extend( glob.glob(os.path.join(results_dir, "**", "console.log"), recursive=True) ) logs = sorted(list(set(candidate_files)), key=os.path.getmtime) for f in reversed(logs[-20:]): try: with open(f, "r", encoding="utf-8", errors="ignore") as fp: content = fp.read() if "loss=" not in content and "step" not in content: continue lines = content.splitlines()[-300:] loss_vals = [] history = [] cur_step, total_step = 1, 50 for l in lines: m = re.search(r"(?:fine-tune\s+step|step)\s+(\d+)/(\d+)\s+loss=([0-9.]+)", l) if m: cur_step = int(m.group(1)) total_step = int(m.group(2)) lv = round(float(m.group(3)), 4) loss_vals.append(lv) history.append({"step": cur_step, "total_steps": total_step, "loss": lv}) else: lm = re.search(r"loss=([0-9.]+)", l) if lm: lv = round(float(lm.group(1)), 4) loss_vals.append(lv) if not loss_vals: continue norm_path = f.replace("\\", "/") mod = target_module or "cur_linear" arch = target_model or "qwen3_5" m_bench = re.search(r"bench_([a-zA-Z0-9_]+)\.log", norm_path) if m_bench: mod = m_bench.group(1) elif "unified_" in norm_path: parts = norm_path.split("/") for i, p in enumerate(parts): if p.startswith("unified_"): mod = p.replace("unified_", "") if i + 1 < len(parts): arch = parts[i + 1] latest_loss = loss_vals[-1] pct = int((cur_step / max(1, total_step)) * 100) if total_step > 0 else 50 eta_sec = max(5, (total_step - cur_step) * 2) return { "module_name": mod, "model_family": arch.capitalize(), "dataset": "SST-2", "seed": 42, "epoch": "1/3", "step": f"{cur_step}/{total_step}", "loss": latest_loss, "progress_percent": min(100, pct), "eta_formatted": f"{eta_sec}s", "loss_trajectory": loss_vals[-50:], "history": history[-50:], "log_file": norm_path, } except Exception: continue return None def get_raw_log_tail(source='active', target_module=None, lines_count=200)-
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def get_raw_log_tail(source="active", target_module=None, lines_count=200): results_dir = "D:/SVD_Project/results" target_file = None if source == "queue": for qf in ["D:/SVD_Project/queue_launcher.log", "D:/SVD_Project/gpu_queue.log"]: if os.path.exists(qf): target_file = qf break elif target_module: m_logs = sorted( glob.glob( os.path.join(results_dir, f"unified_{target_module}", "**", "console.log"), recursive=True, ), key=os.path.getmtime, ) if m_logs: target_file = m_logs[-1] if not target_file: logs = sorted( glob.glob(os.path.join(results_dir, "**", "console.log"), recursive=True), key=os.path.getmtime, ) if logs: target_file = logs[-1] if target_file and os.path.exists(target_file): try: with open(target_file, "r", encoding="utf-8", errors="ignore") as fp: all_lines = fp.readlines() clean = [ l.rstrip("\r\n") for l in all_lines[-lines_count:] if not l.startswith("**") ] return {"file": target_file.replace("\\", "/"), "lines": clean, "count": len(clean)} except Exception as e: return {"file": target_file, "lines": [f"Error reading log: {e}"], "count": 1} return {"file": "none", "lines": ["No log file found on GPU server."], "count": 1} def get_telemetry()-
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def get_telemetry(): telemetry = { "gpu_name": "NVIDIA RTX 4070 (8GB)", "mem_used_mb": 450, "mem_total_mb": 8188, "gpu_util": 0, "temp_c": 45, "power_w": 25.0, } try: out = subprocess.check_output( "nvidia-smi --query-gpu=name,memory.used,memory.total,utilization.gpu,temperature.gpu,power.draw --format=csv,noheader,nounits", shell=True, text=True, timeout=3, ).strip() parts = [p.strip() for p in out.split(",")] if len(parts) >= 6: telemetry = { "gpu_name": parts[0], "mem_used_mb": int(float(parts[1])), "mem_total_mb": int(float(parts[2])), "gpu_util": int(float(parts[3])), "temp_c": int(float(parts[4])), "power_w": float(parts[5]), } except Exception: pass return telemetry