We will use generative adversarial networks to generate new images of faces.
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Create an account on floydhub.com (don't forget to confirm your email). You will automatically receive 100 free GPU hours.
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Install the
floyd
command on your computer:pip install -U floyd-cli
Do this even if you already installed
floyd-cli
before, just to make sure you have the most recent version (Its pace of development is fast!). -
Associate the command with your Floyd account:
floyd login
(a page with authentication token will open; you will need to copy the token into your terminal)
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Clone the repository:
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Enter the folder for the image classification project:
cd face-generation
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Initiate a Floyd project:
floyd init face-generation
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Run the project:
floyd run --gpu --env tensorflow --mode jupyter --data diSgciLH4WA7HpcHNasP9j
It will be run on a machine with GPU (
--gpu
), using a Tenserflow environment (--env tensorflow
), as a Jupyter notebook (--mode jupyter
), with Floyd's built-in cifar-10 dataset available (--data diSgciLH4WA7HpcHNasP9j
). -
Wait for the Jupyter notebook to become available and then access the URL displayed in the terminal (described as "path to jupyter notebook"). You will see the notebook.
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Remember to explicitly stop the experiment when you are not using the notebook. As long as it runs (even in the background) it will cost GPU hours. You can stop an experiment in the "Experiments" section on floyd.com or using the
floyd stop
command:floyd stop ID
(where ID is the "RUN ID" displayed in the terminal when you run the project; if you lost it you can also find it in the "Experiments" section on floyd.com)