Run the shell or python command to obtain the GPU usage.

Using the shell Command

Run the nvidia-smi command.
This operation relies on CUDA NVCC.
```
watch -n 1 nvidia-smi
```
Run the gpustat command.
```
pip install gpustat 
```
```
gpustat -cp -i
```
To stop the command execution, press Ctrl+C.

Using the python Command

Run the nvidia-ml-py3 command (commonly used).

!pip install nvidia-ml-py3

import nvidia_smi
nvidia_smi.nvmlInit()
deviceCount = nvidia_smi.nvmlDeviceGetCount()
for i in range(deviceCount):
    handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)
    util = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
    mem = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
    print(f"|Device {i}| Mem Free: {mem.free/1024**2:5.2f}MB / {mem.total/1024**2:5.2f}MB | gpu-util: {util.gpu:3.1%} | gpu-mem: {util.memory:3.1%} |")

Click to enlarge

Run the nvidia_smi, wapper, and prettytable commands.

Use the decorator to obtain the GPU usage in real time during model training.

def gputil_decorator(func):
    def wrapper(*args, **kwargs):
        import nvidia_smi
        import prettytable as pt

        try:
            table = pt.PrettyTable(['Devices','Mem Free','GPU-util','GPU-mem'])
            nvidia_smi.nvmlInit()
            deviceCount = nvidia_smi.nvmlDeviceGetCount()
            for i in range(deviceCount):
                handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)
                res = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
                mem = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
                table.add_row([i, f"{mem.free/1024**2:5.2f}MB/{mem.total/1024**2:5.2f}MB", f"{res.gpu:3.1%}", f"{res.memory:3.1%}"])

        except nvidia_smi.NVMLError as error:
            print(error)

        print(table)
        return func(*args, **kwargs)
    return wrapper

Click to enlarge

Run the pynvml command.

Run nvidia-ml-py3 to directly obtain the nvml c-lib library, without using nvidia-smi. Therefore, this command is recommended.

from pynvml import *
nvmlInit()
handle = nvmlDeviceGetHandleByIndex(0)
info = nvmlDeviceGetMemoryInfo(handle)
print("Total memory:", info.total)
print("Free memory:", info.free)
print("Used memory:", info.used)

Run the gputil command.

!pip install gputil

import GPUtil as GPU
GPU.showUtilization()

Click to enlarge

import GPUtil as GPU
GPUs = GPU.getGPUs()
for gpu in GPUs:
    print("GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))

Click to enlarge

When using a deep learning framework such as PyTorch or TensorFlow, you can also use the APIs provided by the framework for query.

How Can I Obtain GPU Usage Through Code?

Using the shell Command

Using the python Command