In a September Meadow
The oak trees drip with acorns, squirrels chatter in the canopy. “In a September Meadow” is published by Hannah Miller in The Junction.
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How to run PyTorch with GPU and CUDA 9.2 support on Google Colab
It has been a while since I wrote my first tutorial about running deep learning experiments on Google’s GPU enabled Jupyter notebook interface- Colab. Since then, my several blogs have walked through running either Keras, TensorFlow or Caffe on Colab with GPU accelerated.
One missing framework not pre-installed on Colab is PyTorch. Recently, I am checking out a video to video synthesis model requires running on Linux, plus there are gigabytes of data and pre-trained model to download before I can take the shiny model for a spin. I was wondering, why not give Colab a try by leveraging its awesome downloading speed and freely available GPU?
Let’s starts by installing CUDA on Colab.
Why not other CUDA versions? Here are three reasons.
It returns the information you want.
After that, you will be able to navigate through the target platform selections, make the installer type “deb(local)”, then right click on the “Download (1.2 GB)” button to copy the link address.
Run the following cell to complete the CUDA installation.
If you see those lines at the end of the output, that means the installing was successful.
Continue with Pytorch.
It will let you run this line below, after which, the installation is done!
Besides, the demo also depends on custom built CUDA extensions gives the chance to test out the installed CUDA toolkit.
The cell below does all the job from getting the code to running the demo with the pre-trained model.
That wraps up this tutorial.
Here are some of my previous Colab tutorials.
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