Merge pull request #211 from rakuri255/show_gpu

show gpu name and vram

ReleaseNotes.md

@@ -1,4 +1,4 @@
-# Version: 0.0.13dev3
+# Version: 0.0.13dev4
 Date: 2024.12.26
 - Changes:
   - Download Cover from MusicBrainz
@@ -8,6 +8,7 @@ Date: 2024.12.26
   - Improved arguments, so you dont need to use true or false
   - Added interactive mode
   - Use user defined ffmpeg path
+  - Show GPU Name and VRAM
 
 # Version: 0.0.12
 Date: 2024.12.19

src/Settings.py

@@ -11,7 +11,7 @@
 @dataclass
 class Settings:
 
-    APP_VERSION = "0.0.13-dev3"
+    APP_VERSION = "0.0.13-dev4"
     CONFIDENCE_THRESHOLD = 0.6
     CONFIDENCE_PROMPT_TIMEOUT = 4
 

src/modules/DeviceDetection/device_detection.py

@@ -14,22 +14,40 @@ def check_gpu_support() -> tuple[bool, bool]:
 
     print(f"{ULTRASINGER_HEAD} Checking GPU support.")
 
+    pytorch_gpu_supported = __check_pytorch_support()
+    tensorflow_gpu_supported = __check_tensorflow_support()
+
+    return 'cuda' if tensorflow_gpu_supported else 'cpu', 'cuda' if pytorch_gpu_supported else 'cpu'
+
+
+def __check_tensorflow_support():
     tensorflow_gpu_supported = False
     gpus = tf.config.list_physical_devices('GPU')
     if gpus:
         tensorflow_gpu_supported = True
         print(f"{ULTRASINGER_HEAD} {blue_highlighted('tensorflow')} - using {red_highlighted('cuda')} gpu.")
     else:
-        print(f"{ULTRASINGER_HEAD} {blue_highlighted('tensorflow')} - there are no {red_highlighted('cuda')} devices available -> Using {red_highlighted('cpu')}.")
+        print(
+            f"{ULTRASINGER_HEAD} {blue_highlighted('tensorflow')} - there are no {red_highlighted('cuda')} devices available -> Using {red_highlighted('cpu')}.")
         if os.name == 'nt':
-            print(f"{ULTRASINGER_HEAD} {blue_highlighted('tensorflow')} - versions above 2.10 dropped GPU support for Windows, refer to the readme for possible solutions.")
+            print(
+                f"{ULTRASINGER_HEAD} {blue_highlighted('tensorflow')} - versions above 2.10 dropped GPU support for Windows, refer to the readme for possible solutions.")
+    return tensorflow_gpu_supported
+
 
+def __check_pytorch_support():
     pytorch_gpu_supported = torch.cuda.is_available()
     if not pytorch_gpu_supported:
         print(
             f"{ULTRASINGER_HEAD} {blue_highlighted('pytorch')} - there are no {red_highlighted('cuda')} devices available -> Using {red_highlighted('cpu')}."
         )
     else:
+        gpu_name = torch.cuda.get_device_name(0)
+        gpu_properties = torch.cuda.get_device_properties(0)
+        gpu_vram = round(gpu_properties.total_memory / 1024 ** 3, 2)  # Convert bytes to GB and round to 2 decimal places
+        print(f"{ULTRASINGER_HEAD} Found GPU: {blue_highlighted(gpu_name)} VRAM: {blue_highlighted(gpu_vram)} GB.")
+        if gpu_vram < 6:
+            print(
+                f"{ULTRASINGER_HEAD} {red_highlighted('GPU VRAM is less than 6GB. Program may crash due to insufficient memory.')}")
         print(f"{ULTRASINGER_HEAD} {blue_highlighted('pytorch')} - using {red_highlighted('cuda')} gpu.")
-
-    return 'cuda' if tensorflow_gpu_supported else 'cpu', 'cuda' if pytorch_gpu_supported else 'cpu'
+    return pytorch_gpu_supported