Known Issues

• Nsight Python launches the application python script again with NVIDIA Nsight Compute CLI (ncu) for each @nsight.analyze.kernel decorator.

  • Due to this issue, using nsight-python in interactive notebook environments (e.g., Jupyter Notebook or Google Colab) is not supported. Refer to GitHub issue #3 for more information..

  In the meantime, you can use the following workaround by placing your benchmark into a standalone Python script and executing it outside the interactive cell environment:

  %%writefile quickstart.py
  import torch
  import nsight
  
  @nsight.analyze.kernel
  def benchmark_matmul(n):
      """
      The simplest possible benchmark.
      We create two matrices and multiply them.
      """
      # Create two NxN matrices on GPU
      a = torch.randn(n, n, device="cuda")
      b = torch.randn(n, n, device="cuda")
  
      # Mark the kernel we want to profile
      with nsight.annotate("matmul"):
          c = a @ b
  
      return c
  
  result = benchmark_matmul(1024)
  

  Then run the script with:

  %run quickstart.py
  

• Kernels launched from a subprocess which is created within the annotated region will not be profiled.

• For the nsight.analyze.kernel’s replay_mode="range" option, only a subset of CUDA APIs are supported within the annotated range. If an unsupported API call is detected, an error will be reported. For details on supported APIs, refer to the NVIDIA Nsight Compute Profiling Guide. In such cases, you can either switch to replay_mode="kernel" or modify the code to exclude the unsupported API from the annotated range.

• Nested annotations (using nsight.annotate within another nsight.annotate context) are not supported. nsight-python errors out when nested annotations are used.