Socket
Socket
Sign inDemoInstall

gfx2cuda

Package Overview
Dependencies
2
Maintainers
1
Alerts
File Explorer

Install Socket

Detect and block malicious and high-risk dependencies

Install

    gfx2cuda

Fast graphics texture to cuda transfer


Maintainers
1

Readme

Gfx2Cuda - Graphics to CUDA interoperability

Gfx2Cuda is a python implementation of CUDA's graphics interopability methods for DirectX, OpenGL, etc. The main usage is for quick transfer of images rendered with for example Godot or Unity to CUDA memory buffers such as pytoch tensors, without needing to transfer the image to cpu and back to gpu.

For now only DirectX 11 is supported. This can be useful for implementing CUDA ipc (interprocess-communication) for Windows, since that functionality is not available in vanilla CUDA for Windows. You would use a DirectX texture as buffer that can be seen by multiple processes without having to download any gpu data to cpu and back.

Example

Render to texture and copy to pytorch tensor

import gfx2cuda
import torch

# Shape: [height, width, channels]
shape = [480, 640, 4]
tensor1 = torch.ones(shape).contiguous().cuda()
tensor2 = torch.zeros(shape).contiguous().cuda()

# Create copy of a tensor but as a texture
tex = gfx2cuda.texture(tensor1)

with tex as ptr:
    tex.copy_to(tensor2)

print(tensor2.data)
# pytorch tensor should now contain a copy of the texture data

Share texture between process, write on one process and see results in the other

from multiprocessing import Process

import gfx2cuda
import torch

shape = [4, 4, 4]

def f(handle):
    tex = gfx2cuda.open_ipc_texture(handle)
    # Received and opened the texture
    print(tex)
    # >> Texture with format TextureFormat.RGBA32FLOAT (4 x 4)
    tensor1 = torch.ones(shape).contiguous().cuda()
    with tex:
        tex.copy_from(tensor1)

if __name__ == "__main__":
    tensor = torch.zeros(shape).contiguous().cuda()
    # Initialize as all zeros
    tex = gfx2cuda.texture(tensor)

    p = Process(target=f, args=(tex.ipc_handle,))
    p.start()
    p.join()

    with tex:
        tex.copy_to(tensor)

    print(tensor.data)
    # See all ones

Keywords

FAQs


Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc