Set up and run Jupyter Notebooks on Thunder Compute’s affordable cloud GPUs. Connect via VSCode, install extensions, and verify GPU access for ML/data science.
Create a Jupyter Notebook, which is now connected to a Thunder Compute instance with GPU capabilities. To confirm that the GPU is accessible, run the following in a notebook cell:
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import torchprint(torch.cuda.is_available())
If everything is set up correctly, the output should be:
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True
You now have a Jupyter Notebook running on a Thunder Compute cloud GPU, a fast and low-cost alternative to Colab for indie developers, researchers, and data scientists.