AWS CloudFormation vs. PyTorch on AWS
| Product | Rating | Most Used By | Product Summary | Starting Price |
|---|---|---|---|---|
 AWS CloudFormation | Score8.8 out of 10 | N/A | AWS CloudFormation gives developers and systems administrators a way to create and manage a collection of related AWS resources, provisioning and updating them in a predictable fashion. Use AWS CloudFormation’s sample templates or create templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run an application. Users don’t need to figure out the order for provisioning AWS services or the subtleties of making those dependencies work.… | $0 |
PyTorch on AWS | Score10 out of 10 | N/A | PyTorch is an open source deep learning framework. PyTorch on AWS using Amazon SageMaker presents a fully managed machine learning service used to build, train, and deploy PyTorch models at scale. For managing one's own infrastructure, one can use the AWS Deep Learning AMIs or the AWS Deep Learning Containers, which come pre-installed with PyTorch to deploy custom machine learning environments. | N/A |
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| Entry-level Setup Fee | No setup fee | No setup fee | ||||||||||||||
| Additional Details | There is no additional charge for using AWS CloudFormation with resource providers in the following namespaces: AWS::*, Alexa::*, and Custom::*. In this case you pay for AWS resources (such as Amazon EC2 instances, Elastic Load Balancing load balancers, etc.) created using AWS CloudFormation as if you created them manually. You only pay for what you use, as you use it; there are no minimum fees and no required upfront commitments. When you use resource providers with AWS CloudFormation outside the namespaces mentioned above, you incur charges per handler operation. Handler operations are create, update, delete, read, or list actions on a resource. | — | ||||||||||||||
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| AWS CloudFormation | PyTorch on AWS | |
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| Likelihood to Recommend | 7.6 | - |