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
},
"bfloat16":{
"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'<a target="_blank" href="{dataset_url}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{dataset_name}</a>'
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_pattern = re.compile(r'NVIDIA\s+(RTX\s+)?([A-Z0-9]+)')
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 = ''.join(filter(None, name_match.groups())).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_details():
gpus = GPUtil.getGPUs()
gpu = gpus[0]
name = gpu.name.replace(" ", "-")
memory_gb = round(gpu.memoryTotal / 1024)
memory = f"{memory_gb}GB"
for part in name.split('-'):
if part.endswith("GB") and part[:-2].isdigit():
name = name.replace(f"-{part}", "").replace(part, "")
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()))