From 0d483f3af00d6e0c99a27a84ef22d41549412bab Mon Sep 17 00:00:00 2001
From: luclmt <102718301+luclmt@users.noreply.github.com>
Date: Thu, 17 Jul 2025 11:41:18 +0200
Subject: [PATCH] Update networks.py

Use torch instead of nvidia-smi to get free mem gpu
---
 clearvoice/clearvoice/networks.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/clearvoice/clearvoice/networks.py b/clearvoice/clearvoice/networks.py
index e46c125..7ae97aa 100644
--- a/clearvoice/clearvoice/networks.py
+++ b/clearvoice/clearvoice/networks.py
@@ -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
@@ -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