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Chapter 12You can learn about all the services offered by AWS using this link: https://docs.aws.amazon.com/index.html?nc2=h_ql_doc_do. Let us now go through some ofthe important AWS services that we as deep learning engineers/researchers can use:• Elastic Compute Cloud (EC2): Provides virtual computers. You canconfigure the hardware and software according to your infrastructural needs.You have an option to choose from CPU, GPU, storage, networking, and diskimage configurations. We will talk about how to create an EC2 instance fordeep learning in the next section.[ 443 ]
TensorFlow and Cloud• Lambda: The serverless computer service offered by Amazon. It lets you runcode without provisioning or managing servers. You only need to pay forthe compute time you consume – there is no charge when your code is notrunning. It allows one to run code for virtually any type of application orbackend service, with zero administration requirements.• Elastic Beanstalk: Provides quick and efficient services for deployment,monitoring, and scaling of your application.• AWS IoT: Allows you to connect and manage devices in the cloud.• SageMaker: A platform for developing and deploying machine learningmodels. With its prebuilt ML models, it allows you to train and deployML algorithms with ease. Later in this chapter we will learn how to use theintegrated Jupyter Notebook of SageMaker to train our model on cloud.Google Cloud Platform (GCP)From computing infrastructure to software management, GCP provides a suiteof cloud computing services. A complete list of all the services offered by GCP isavailable here: https://cloud.google.com/docs/. Google cloud offers the sameinfrastructure that it uses for its end-user products like Gmail, Google Search, andYouTube. Beside CPUs and GPUs, GCP also offers a choice of TPUs (Chapter 16,Tensor Processing Unit).GCP allows you to open an account for free – you just need to register using anemail address (or phone) and card (debit/credit) details. It offers a $300 credit to newusers, which is valid for 12 months and can be used across its products. Once you login to the Google console you can access all its services. Following is a screenshot ofmy Google console:[ 444 ]
- Page 428 and 429: Chapter 10ρρ(vv oo |h oo ) = σσ
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- Page 432 and 433: Chapter 10And the reconstructed ima
- Page 434 and 435: Chapter 10inpX = rbm.rbm_output(inp
- Page 436 and 437: Chapter 10(60000, 28, 28) (60000,)(
- Page 438 and 439: Chapter 10Figure 11: Summary of the
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- Page 442 and 443: Reinforcement LearningThis chapter
- Page 444 and 445: Chapter 11And unlike unsupervised l
- Page 446 and 447: Chapter 11Normally, the value is de
- Page 448 and 449: Chapter 11• The next question tha
- Page 450 and 451: Chapter 11This neural network takes
- Page 452 and 453: Chapter 11The MuJoCo environment re
- Page 454 and 455: Chapter 11We will first import the
- Page 456 and 457: Chapter 11The αα is the learning
- Page 458 and 459: Chapter 11We set up the global valu
- Page 460 and 461: Chapter 11else:return np.argmax(sel
- Page 462 and 463: Chapter 11DQN to play a game of Ata
- Page 464 and 465: Chapter 11self.model.add( Conv2D(64
- Page 466 and 467: Chapter 11Here the action A was sel
- Page 468 and 469: Chapter 11Image source: https://arx
- Page 470 and 471: Chapter 11A neural network is used
- Page 472: Chapter 1111. Details regarding ins
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- Page 527 and 528: An introduction to AutoMLThat is pr
Chapter 12
You can learn about all the services offered by AWS using this link: https://docs.
aws.amazon.com/index.html?nc2=h_ql_doc_do. Let us now go through some of
the important AWS services that we as deep learning engineers/researchers can use:
• Elastic Compute Cloud (EC2): Provides virtual computers. You can
configure the hardware and software according to your infrastructural needs.
You have an option to choose from CPU, GPU, storage, networking, and disk
image configurations. We will talk about how to create an EC2 instance for
deep learning in the next section.
[ 443 ]
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