For tinkering around, just use Googe Colab[1]. They offer free hosted Jupyter notebooks and have both Nvidia GPU[2] and Google TPU[3] runtime options available.

For tinkering around, just use Googe Colab[1]. They offer free hosted Jupyter notebooks and have both Nvidia GPU[2] and Google TPU[3] runtime options available. Here[4] is a notebook that shows how to install CUDA into an environment using the GPU accelerated runtime. Only major downside is that resources aren't guaranteed (see first section under "Resource Limits" here[5]), so you sporadically may not be able to start a GPU-accelerated runtime session. But that shouldn't be much of a blocker for tinkering purposes. [1] https://colab.research.google.com/notebooks/intro.ipynb https://colab.research.google.com/notebooks/intro.ipynb [2] https://colab.research.google.com/notebooks/gpu.ipynb https://colab.research.google.com/notebooks/gpu.ipynb [3] https://colab.research.google.com/notebooks/tpu.ipynb https://colab.research.google.com/notebooks/tpu.ipynb [4] https://colab.research.google.com/github/ShimaaElabd/CUDA-GPU-Contrast-Enhancement/blob/master/CUDA_GPU.ipynb https://colab.research.google.com/github/ShimaaElabd/CUDA-GP... [5] https://research.google.com/colaboratory/faq.html https://research.google.com/colaboratory/faq.html