import pandas as pd from huggingface_hub import snapshot_download import subprocess import re import os import GPUtil try: from src.display.utils import GPU_TEMP, GPU_Mem, GPU_Power, GPU_Util, GPU_Name except: print("local debug: from display.utils") from display.utils import GPU_TEMP, GPU_Mem, GPU_Power, GPU_Util, GPU_Name MEM_BW_DICT ={ "NVIDIA-A100-PCIe-80GB": 1935, "NVIDIA-A100-SXM-80GB": 2039, "NVIDIA-H100-PCIe-80GB": 2039, "NVIDIA-RTX-A5000-24GB": 768 } PEAK_FLOPS_DICT = { "float32":{ "NVIDIA-A100-PCIe-80GB": 312e12, "NVIDIA-A100-SXM-80GB": 312e12, "NVIDIA-H100-PCIe-80GB": 756e12, "NVIDIA-RTX-A5000-24GB": 222.2e12 }, "float16":{ "NVIDIA-A100-PCIe-80GB": 624e12, "NVIDIA-A100-SXM-80GB": 624e12, "NVIDIA-H100-PCIe-80GB": 1513e12, "NVIDIA-RTX-A5000-24GB": 444.4e12 }, "8bit":{ "NVIDIA-A100-PCIe-80GB": 1248e12, "NVIDIA-A100-SXM-80GB": 1248e12, "NVIDIA-H100-PCIe-80GB": 3026e12, "NVIDIA-RTX-A5000-24GB": 889e12 }, "4bit": { "NVIDIA-A100-PCIe-80GB": 2496e12, "NVIDIA-A100-SXM-80GB": 2496e12, "NVIDIA-H100-PCIe-80GB": 6052e12, "NVIDIA-RTX-A5000-24GB": 1778e12 } } def my_snapshot_download(repo_id, revision, local_dir, repo_type, max_workers): for i in range(10): try: snapshot_download( repo_id=repo_id, revision=revision, local_dir=local_dir, repo_type=repo_type, max_workers=max_workers ) return except Exception as e: print(f"Failed to download {repo_id} at {revision} with error: {e}. Retrying...") import time time.sleep(60) return def get_dataset_url(row): dataset_name = row["Benchmark"] dataset_url = row["Dataset Link"] benchmark = f'{dataset_name}' return benchmark def get_dataset_summary_table(file_path): df = pd.read_csv(file_path) df["Benchmark"] = df.apply(lambda x: get_dataset_url(x), axis=1) df = df[["Category", "Benchmark", "Data Split", "Data Size", "Language"]] return df def parse_nvidia_smi(): visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', None) if visible_devices is not None: gpu_indices = visible_devices.split(',') else: # Query all GPU indices if CUDA_VISIBLE_DEVICES is not set result = subprocess.run(['nvidia-smi', '--query-gpu=index', '--format=csv,noheader'], capture_output=True, text=True) if result.returncode != 0: print("Failed to query GPU indices.") return [] gpu_indices = result.stdout.strip().split('\n') # print(f"gpu_indices: {gpu_indices}") gpu_stats = [] gpu_info_pattern = re.compile(r'(\d+)C\s+P\d+\s+(\d+)W / \d+W\s+\|\s+(\d+)MiB / \d+MiB\s+\|\s+(\d+)%') gpu_name_pattern = re.compile(r'NVIDIA\s+([\w\s]+\d+(?:\s*GB)?)') gpu_name = "" for index in gpu_indices: result = subprocess.run(['nvidia-smi', '-i', index], capture_output=True, text=True) output = result.stdout.strip() lines = output.split("\n") for line in lines: match = gpu_info_pattern.search(line) name_match = gpu_name_pattern.search(line) gpu_info = {} if name_match: gpu_name = name_match.group(1).strip() if match: temp, power_usage, mem_usage, gpu_util = map(int, match.groups()) gpu_info.update({ GPU_TEMP: temp, GPU_Power: power_usage, GPU_Mem: round(mem_usage / 1024, 2), GPU_Util: gpu_util }) if len(gpu_info) >= 4: gpu_stats.append(gpu_info) # print(f"gpu_stats: {gpu_stats}") gpu_name = f"{len(gpu_stats)}x{gpu_name}" gpu_stats_total = { GPU_TEMP: 0, GPU_Power: 0, GPU_Mem: 0, GPU_Util: 0, GPU_Name: gpu_name } for gpu_stat in gpu_stats: gpu_stats_total[GPU_TEMP] += gpu_stat[GPU_TEMP] gpu_stats_total[GPU_Power] += gpu_stat[GPU_Power] gpu_stats_total[GPU_Mem] += gpu_stat[GPU_Mem] gpu_stats_total[GPU_Util] += gpu_stat[GPU_Util] gpu_stats_total[GPU_Mem] = gpu_stats_total[GPU_Mem] # G gpu_stats_total[GPU_TEMP] /= len(gpu_stats) gpu_stats_total[GPU_Power] /= len(gpu_stats) gpu_stats_total[GPU_Util] /= len(gpu_stats) return [gpu_stats_total] def monitor_gpus(stop_event, interval, stats_list): while not stop_event.is_set(): gpu_stats = parse_nvidia_smi() if gpu_stats: stats_list.extend(gpu_stats) stop_event.wait(interval) def analyze_gpu_stats(stats_list): # Check if the stats_list is empty, and return None if it is if not stats_list: return None # Initialize dictionaries to store the stats avg_stats = {} max_stats = {} # Calculate average stats, excluding 'GPU_Mem' for key in stats_list[0].keys(): if key != GPU_Mem and key != GPU_Name: total = sum(d[key] for d in stats_list) avg_stats[key] = total / len(stats_list) # Calculate max stats for 'GPU_Mem' max_stats[GPU_Mem] = max(d[GPU_Mem] for d in stats_list) if GPU_Name in stats_list[0]: avg_stats[GPU_Name] = stats_list[0][GPU_Name] # Update average stats with max GPU memory usage avg_stats.update(max_stats) return avg_stats def get_gpu_number(): visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', None) if visible_devices is not None: gpu_indices = visible_devices.split(',') else: # Query all GPU indices if CUDA_VISIBLE_DEVICES is not set result = subprocess.run(['nvidia-smi', '--query-gpu=index', '--format=csv,noheader'], capture_output=True, text=True) if result.returncode != 0: print("Failed to query GPU indices.") return [] gpu_indices = result.stdout.strip().split('\n') # print(f"gpu_indices: {gpu_indices}") gpu_stats = [] gpu_info_pattern = re.compile(r'(\d+)C\s+P\d+\s+(\d+)W / \d+W\s+\|\s+(\d+)MiB / \d+MiB\s+\|\s+(\d+)%') for index in gpu_indices: result = subprocess.run(['nvidia-smi', '-i', index], capture_output=True, text=True) output = result.stdout.strip() lines = output.split("\n") for line in lines: match = gpu_info_pattern.search(line) gpu_info = {} if match: temp, power_usage, mem_usage, gpu_util = map(int, match.groups()) gpu_info.update({ GPU_TEMP: temp, GPU_Power: power_usage, GPU_Mem: round(mem_usage / 1024, 2), GPU_Util: gpu_util }) if len(gpu_info) >= 4: gpu_stats.append(gpu_info) return len(gpu_stats) def get_gpu_details(): gpus = GPUtil.getGPUs() gpu = gpus[0] name = gpu.name.replace(" ", "-") # Convert memory from MB to GB and round to nearest whole number memory_gb = round(gpu.memoryTotal / 1024) memory = f"{memory_gb}GB" formatted_name = f"{name}-{memory}" return formatted_name def get_peak_bw(gpu_name): return MEM_BW_DICT[gpu_name] def get_peak_flops(gpu_name, precision): return PEAK_FLOPS_DICT[precision][gpu_name] def transfer_precision2bytes(precision): if precision == "float32": return 4 elif precision in ["float16", "bfloat16"]: return 2 elif precision == "8bit": return 1 elif precision == "4bit": return 0.5 else: raise ValueError(f"Unsupported precision: {precision}") if __name__ == "__main__": print(analyze_gpu_stats(parse_nvidia_smi()))