How to Train Keras Deep Learning Models on AWS EC2 GPUs (step-by-step)
Last Updated on August 19, 2019
Keras is a Python deep learning library that provides easy and convenient access to the powerful numerical libraries like TensorFlow.
Large deep learning models require a lot of compute time to run. You can run them on your CPU but it can take hours or days to get a result. If you have access to a GPU on your desktop, you can drastically speed up the training time of your deep learning models.
In this post, you will discover how you can get access to GPUs to speed up the training of your deep learning models by using the Amazon Web Service (AWS) infrastructure. For a few dollars per hour and often a lot cheaper you can use this service from your workstation or laptop.
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Let’s get started.
- Update Oct/2016: Updated examples for Keras 1.1.0.
- Update Mar/2017: Updated to use new AMI, Keras 2.0.2 and TensorFlow 1.0.
- Update Feb/2019: Updated to use the new “Deep Learning AMI” and “p3.2xlarge”.