Environment

• Pythonnet version: 3.0.3

• Python version: 3.10.14

• Environment Versions
  PyTorch version: 2.3.0+cpu
  Is debug build: False
  Python platform: Windows-10-10.0.22631-SP0
  Is CUDA available: False
  CUDA runtime version: 12.4.99
  CUDA_MODULE_LOADING set to: N/A
  GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4060 Laptop GPU
  Nvidia driver version: 556.12
  cuDNN version: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin\cudnn_ops_train64_8.dll
  HIP runtime version: N/A
  MIOpen runtime version: N/A
  Is XNNPACK available: True

  Versions of relevant libraries:
  [pip3] numpy==1.26.4
  [pip3] torch==2.3.0
  [pip3] torchaudio==2.3.0
  [pip3] torchvision==0.18.0
  [conda] blas 1.0 mkl
  [conda] mkl 2021.4.0 pypi_0 pypi
  [conda] mkl-service 2.4.0 py310h2bbff1b_1
  [conda] mkl_fft 1.3.8 py310h2bbff1b_0
  [conda] mkl_random 1.2.4 py310h59b6b97_0
  [conda] numpy 1.26.4 py310h055cbcc_0
  [conda] numpy-base 1.26.4 py310h65a83cf_0
  [conda] torch 2.3.0 pypi_0 pypi
  [conda] torchaudio 2.3.0 pypi_0 pypi
  [conda] torchvision 0.18.0 pypi_0 pypi

• Operating System:
  OS: Microsoft Windows 11
  GCC version: Could not collect
  Clang version: Could not collect
  CMake version: version 3.29.0-rc3
  Libc version: N/A

• .NET Runtime:
  .NET framework 4.8

import torch
para =torch.Tensor(initial_para)
x,y  = torch.tensor(x),torch.relu(torch.tensor(y))
para.requires_grad = True
loss = torch.nn.MSELoss()
optimizer = torch.optim.Adam([para], lr=lr)
for i in `range(max_iter):`
    y_fit = multi_gaussian(x,para)
    l = loss(y_fit,y)
    try:
        if torch.isnan(l) or torch.isinf(l) :
            print("l is nan or inf")
            break
        l.backward()
        optimizer.step()
        optimizer.zero_grad()
    except:
        break
def multi_gaussian(x, params):
    y = torch.zeros_like(x)
    for i in range(0, params.shape[0], 3):
        g = gaussian(x, params[i], params[i + 1], params[i + 2])
        if torch.any(torch.isnan(g)) or torch.any(torch.isinf(g)):
            continue
        y = y + g
    return y
def gaussian(x,center,amp,variance, offset = None):
    return amp*torch.exp(-(x-center)**2/(variance ))

Details

Hi, im using pytorch to fit gaussian mixture model. It works perfect via directly run main.py. My data had length of 1024, and it only took less than 0.1 s to fit the model. However, a strange issue occurred when I using PythonNet package in CSharp. After the python environment was initialized, the main.py was also successfully runned. But the time for model fit increased to nearly 1.8 s, 18 times slower than previous condition.
While i used another fit methed named scipy.optimize.curve_fit method, it tooks almost the same time (0.03 s) as run main.py directly or by CSharp code call. I was so confused by the difference, and I want to know if there is way to solve the problem.

(By the way, im coding my program in net4.8 framework, i cant using torchsharp in Csharp which need net6.0).

Thank a lot!