On Gpu sharing mechanism, specify model.to(' CPU '), but still use the Gpu
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## Details
<!-- Description of your issue -->
You know that The GPU device(K8s) only supports one container exclusive GPU, In the inferencing stage, it is extremely wasteful. For Tiny-Albert model,It's only using about 500MiB。We try to use  GPU share device, support more containers use one GPU device。We expect using  torch.cuda.is_available() to control Using CUDA or Not.
Here is My Code:
`
use_cuda = bool(os.environ.get("USE_CUDA"))
if use_cuda and torch.cuda.is_available():
    self._device = torch.device("cuda")
else:
   self._device = torch.device("cpu")
` 

1. USE_CUDA== False and  torch.cuda.is_available() is True:
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|============================================================|
|    0     11538      C   /usr/bin/python3                             **342MiB** |
+-----------------------------------------------------------------------------+
2. USE_CUDA== True and  torch.cuda.is_available() is True:
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|============================================================|
|    0     11538      C   /usr/bin/python3                             **342MiB** |
+-----------------------------------------------------------------------------+









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