GPU Usage Profiler

[ItsRajSingh]

Should we use subprocess, profiler or torch profile recorder for GPU Usage

[sourcecode369]

The choice between using subprocess to run external commands (nvidia-smi), using a custom profiler, or leveraging PyTorch's built-in profiler (torch.autograd.profiler) for GPU usage monitoring depends on your specific requirements and the level of detail you need in the profiling results.

1. subprocess for nvidia-smi:

   • Pros:
     • Simple and straightforward for obtaining high-level GPU utilization information.
     • Doesn't require modifications to your code.
   • Cons:
     • Limited to high-level metrics provided by nvidia-smi.
     • May introduce additional overhead due to the subprocess call.

   Use Case: Quick checks of overall GPU usage, especially if you need a general overview and don't need fine-grained details.

   import subprocess
   
   result = subprocess.run(["nvidia-smi", "--query-gpu=utilization.gpu", "--format=csv,nounits,noheader"], stdout=subprocess.PIPE, text=True)
   gpu_usage = int(result.stdout.strip())

2. Custom Profiler:

   • Pros:
     • Provides flexibility to instrument specific parts of your code for detailed profiling.
     • Allows custom metrics and events.
   • Cons:
     • Requires manual instrumentation of your code.
     • Profiling results might not be as detailed as specialized GPU profiling tools.

   Use Case: When you need more control over profiling and want to focus on specific sections of your code for detailed analysis.

   import torch.cuda.profiler as profiler
   
   with profiler.profile():
       # Your code here

3. PyTorch Profiler (torch.autograd.profiler):

   • Pros:
     • Built-in profiler designed for PyTorch, supporting both CPU and GPU profiling.
     • Records detailed information about operations, shapes, and memory usage.
   • Cons:
     • Might introduce overhead, especially when recording fine-grained information.

   Use Case: Comprehensive profiling of PyTorch code, including GPU usage and memory metrics.

   import torch
   
   with torch.autograd.profiler.profile(use_cuda=True) as prof:
       # Your PyTorch code here

Ultimately, the choice depends on your specific monitoring and profiling needs. If you need general GPU usage information and simplicity, subprocess might be sufficient. If you want more detailed profiling within your Python code, consider using PyTorch's profiler or custom profiling. For extremely detailed GPU-specific profiling, you might explore external GPU profiling tools like NVIDIA Nsight Systems or nvprof.