[Docs] Add example usage cases

README.md

@@ -34,6 +34,7 @@ pip install keep-gpu
 
 ## Usage
 
+### Use keep-gpu as a cli tool
 
 ```bash
 keep-gpu
@@ -49,6 +50,28 @@ Specify GPU IDs to run on (default is all available GPUs):
 keep-gpu --gpu-ids 0,1,2
 ```
 
+### Use keep-gpu api in your code
+
+Non-blocking gpu keeping logic with `CudaGPUController`:
+```python
+from keep_gpu.single_gpu_controller.cuda_gpu_controller import CudaGPUController
+ctrl = CudaGPUController(rank=0, interval=0.5)
+# occupy GPU while you do CPU-only work
+# this is non-blocking
+ctrl.keep()
+dataset.process()
+ctrl.release()        # give GPU memory back
+model.train_start()   # now run real GPU training
+```
+
+Use `CudaGPUController` as a context manager:
+```python
+from keep_gpu.single_gpu_controller.cuda_gpu_controller import CudaGPUController
+with CudaGPUController(rank=0, interval=0.5):
+    dataset.process()  # GPU occupied inside this block
+model.train_start()    # GPU free after exiting block
+```
+
 ## Credits
 
 This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.

src/keep_gpu/single_gpu_controller/cuda_gpu_controller.py

@@ -37,7 +37,7 @@ class CudaGPUController(BaseGPUController):
 
     ```python
     ctrl = CudaGPUController(rank=0, interval=0.5)
-    ctrl.start()          # occupy GPU while you do CPU-only work
+    ctrl.keep()          # occupy GPU while you do CPU-only work
     dataset.process()
     ctrl.release()        # give GPU memory back
     model.train_start()   # now run real GPU training