removed gpu_info function (#235)

tests/unit/common/test_common_gpu.py

@@ -1,20 +1,7 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-import torch.cuda as cuda
-from utils_cv.common.gpu import gpu_info, which_processor
-
-
-def test_gpu_info():
-    gpus = gpu_info()
-    # Check if torch.cuda returns the same number of gpus
-    assert cuda.device_count() == len(gpus)
-
-    for i in range(len(gpus)):
-        # Check if torch.cuda returns the same device name
-        assert gpus[i]["device_name"] == cuda.get_device_name(i)
-        # Total memory should be grater than used-memory
-        assert int(gpus[i]["total_memory"]) > int(gpus[i]["used_memory"])
+from utils_cv.common.gpu import which_processor
 
 
 def test_which_processor():

utils_cv/common/gpu.py

@@ -1,55 +1,13 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-import subprocess
-import warnings
-
 from torch.cuda import current_device, get_device_name, is_available
 
 
-def gpu_info():
-    """Get information of GPUs.
-
-    Returns:
-        list: List of gpu information dictionary {device_name, total_memory, used_memory}.
-              Returns an empty list if there is no cuda device available.
-    """
-    gpus = []
-
-    try:
-        output = subprocess.check_output(
-            [
-                "nvidia-smi",
-                "--query-gpu=name,memory.total,memory.used",
-                "--format=csv,nounits,noheader",
-            ],
-            encoding="utf-8",
-        )
-        for o in output.split("\n"):
-            info = o.split(",")
-            if len(info) == 3:
-                gpu = dict()
-                gpu["device_name"] = info[0].strip()
-                gpu["total_memory"] = info[1].strip()
-                gpu["used_memory"] = info[2].strip()
-                gpus.append(gpu)
-    except:
-        pass
-
-    return gpus
-
-
 def which_processor():
     """Check if fastai/torch is using GPU or CPU"""
     if is_available():
         device_nr = current_device()
         print(f"Fast.ai (Torch) is using GPU: {get_device_name(device_nr)}")
-        info = gpu_info()
-        if len(info) > device_nr:
-            gpu = info[device_nr]
-            free = int(gpu["total_memory"]) - int(gpu["used_memory"])
-            print(
-                f"Available / Total memory = {free} / {gpu['total_memory']} (MiB)"
-            )
     else:
         print("Cuda is not available. Fast.ai/Torch is using CPU")