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Python Deep Learning Cookbook

You're reading from   Python Deep Learning Cookbook Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787125193
Length 330 pages
Edition 1st Edition
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Author (1):
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Indra den Bakker Indra den Bakker
Author Profile Icon Indra den Bakker
Indra den Bakker
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Table of Contents (15) Chapters Close

Preface 1. Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks 2. Feed-Forward Neural Networks FREE CHAPTER 3. Convolutional Neural Networks 4. Recurrent Neural Networks 5. Reinforcement Learning 6. Generative Adversarial Networks 7. Computer Vision 8. Natural Language Processing 9. Speech Recognition and Video Analysis 10. Time Series and Structured Data 11. Game Playing Agents and Robotics 12. Hyperparameter Selection, Tuning, and Neural Network Learning 13. Network Internals 14. Pretrained Models

Launching an instance on Google Cloud Platform (GCP)

Another popular cloud provider is Google. Its Google Cloud Platform (GCP) is getting more popular and has as a major benefit—it includes a newer GPU type, NVIDIA P100, with 16 GB of GPU memory. In this recipe, we provide the steps to launch a GPU-enabled compute machine.

Getting ready

Before proceeding with this recipe, you should be familiar with GCP and its cost structure.

How to do it...

  1. You need to request an increase in the GPU quota before you launch a compute instance with a GPU for the first time. Go to https://console.cloud.google.com/projectselector/iam-admin/quotas.
  2. First, select the project you want to use and apply the Metric and Region filters accordingly. The GPU instances should show up as follows:
Figure 1.1: Google Cloud Platform dashboard for increasing the GPU quotas
  1. Select the quota you want to change, click on EDIT QUOTAS, and follow the steps.
  2. You will get an e-mail confirmation when your quota has been increased.
  3. Afterwards, you can create a GPU-enabled machine.
  4. When launching a machine, make sure you tick the Allow HTTP traffic and Allow HTTPs traffic boxes if you want to use a Jupyter notebook. 
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