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use torch.cuda.mem_get_info() instead of nvidia-smi to get free mem gpu #132

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use torch.cuda.mem_get_info() instead of nvidia-smi to get free mem gpu #132

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19 changes: 9 additions & 10 deletions clearvoice/clearvoice/networks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@
import torch.backends.mps as mps
import soundfile as sf
import os
import subprocess
import librosa
from tqdm import tqdm
import numpy as np
Expand Down Expand Up @@ -82,25 +81,25 @@ def __init__(self, args):

def get_free_gpu(self):
"""
Identifies the GPU with the most free memory using 'nvidia-smi' and returns its index.
Identifies the GPU with the most free memory using 'torch.cuda.mem_get_info()' and returns its index.

This function queries the available GPUs on the system and determines which one has
the highest amount of free memory. It uses the `nvidia-smi` command-line tool to gather
GPU memory usage data. If successful, it returns the index of the GPU with the most free memory.
This function queries the available GPUs on the system and for cuda api and determines which one has
the highest amount of free memory. If successful, it returns the index of the GPU with the most free memory.
If the query fails or an error occurs, it returns None.

Returns:
int: Index of the GPU with the most free memory, or None if no GPU is found or an error occurs.
"""
try:
# Run nvidia-smi to query GPU memory usage and free memory
result = subprocess.run(['nvidia-smi', '--query-gpu=memory.used,memory.free', '--format=csv,nounits,noheader'], stdout=subprocess.PIPE)
gpu_info = result.stdout.decode('utf-8').strip().split('\n')
num_gpus = torch.cuda.device_count()
if num_gpus == 0:
print("No GPUs available")
return None

free_gpu = None
max_free_memory = 0
for i, info in enumerate(gpu_info):
used, free = map(int, info.split(','))
for i in range(num_gpus):
free, total = torch.cuda.mem_get_info(i)
if free > max_free_memory:
max_free_memory = free
free_gpu = i
Expand Down