cudacanvas

CudaCanvas: Real-time PyTorch Tensor Image Visualisation in CUDA, Eliminating CPU Transfer

CudaCanvas is a simple Python module that eliminates CPU transfer for Pytorch tensors for displaying and rendering images in the training or evaluation phase, ideal for machine learning scientists and engineers.

Simplified version that directly displays the image without explicit window creation (cudacanvas >= v1.0.1)

import torch
import cudacanvas


#REPLACE THIS with you training loop
while (True):

    #REPLACE THIS with you training code and generation of data
    noise_image = torch.rand((4, 500, 500), device="cuda")

    #Visualise your data in real-time
    cudacanvas.im_show(noise_image)

    #OPTIONAL: Terminate training when the window is closed
    if cudacanvas.should_close():
        #end process if the window is closed
        break

And with explicit window creation

import torch
import cudacanvas

noise_image = torch.rand((4, 500, 500), device="cuda")

cudacanvas.set_image(noise_image)
cudacanvas.create_window()

#replace this with you training loop
while (True):

    cudacanvas.render()

    if cudacanvas.should_close():
        #end process if the window is closed
        break

Installation

Before instllation make sure you have torch with cuda support already installed on your machine

We aligned pytorch and cuda version with our package the supporting packages are torch (2.2.2 or 2.1.2) and (11.8 or 12.1)

Identify your current torch and cuda version

import torch
torch.__version__

Depending on your torch and cuda you can install the relevant cudacanvas package, for the latest 2.2.2+cu121 you can simply download the latest package

pip install cudacanvas

For other torch and cuda packages put the torch and cuda version after that cudacanvas version for example for 2.1.2+cu181 the Cudacanvas package you require is 1.0.1.post212181

pip install cudacanvas==1.0.1.post212181

Support

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