Google Cloud Platform provides us with a wealth of resources to support data science, deep learning, and AI projects. Now all we need to care about is how to design and train models, and the platform manages the rest tasks. In current pandemic environment, the entire process of an AI project from design, coding to deployment, can be done remotely on the Cloud Platform. IMPORTANT: If you get the following notification when you create a VM that contains GPUs. You need to increase your GPU quota.
EDIT* This guide was written for fastai version 1, which at the current date and time (Jan 2018) is in the midst of transitioning to the a newer version, dubbed fastai v2. An updated guide will be coming soon. As a deep learning enthusiast in Malaysia, one of the biggest issues I have is securing a cheap GPU option to run my models on. If you're like me and come from a country where paying $80-$100 every month for AWS GPUs is too expensive, I'll show you how I set up a my GPU on GCP without incurring any cost at all. All of this can be done for free at the start.
In my last blog I summarized a research paper that investigated the use of residual neural networks for the purposes of radio signal classification. In this blog I will get you started with Google Cloud Platform and show you how to build a ResNet signal classifier in Python with Keras. Here is a link to my GitHub with the ResNet code: GitHub. The authors of the paper include a link to the dataset that was used in the experiment so we can conduct our own experiments and training sessions. I will walk you through my experience coding a ResNet and reproducing the experiment from the paper.
Google Cloud Platform now provides machine learning images designed for deep learning practitioners. This article will cover the fundamentals of the Google Deep Learning Images, how they benefit the developer, creating a deep learning instance, and common workflows. The Google Deep Learning images are a set of prepackaged VM images with a deep learning framework ready to be run out of the box. Currently, there are images supporting TensorFlow, PyTorch, and generic high-performance computing, with versions for both CPU-only and GPU-enabled workflows. To understand which set of images is right for your use case, consult the graph below.
In the following steps you create an AI Platform Notebooks instance. In the GCP Console, go to the AI Platform Notebook instances page. On the menu bar, click New Instance add, and then select the TensorFlow 1.x framework. It takes a few minutes for AI Platform Notebooks to create the new instance. Now that you have an AI Platform Notebooks instance, you can download the notebook file for this tutorial.