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.…
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TensorFlow
Score 7.7 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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Pricing
AWS CloudFormation
TensorFlow
Editions & Modules
Free Tier - 1,000 Handler Operations per Month per Account
$0.00
Handler Operation
$0.0009
per handler operation
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Offerings
Pricing Offerings
AWS CloudFormation
TensorFlow
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
Yes
No
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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Community Pulse
AWS CloudFormation
TensorFlow
Features
AWS CloudFormation
TensorFlow
Configuration Management
Comparison of Configuration Management features of Product A and Product B
I still give it an 8 because it's one of those tools that just quietly does the heavy lifting for you but it can really test your patience when it breaks esp with deep nested stacks. It's perfect for projects where we need clean consistent environments every time. It's less ideal for quick experimental setups like new EC2 configs or Lambda permission tweaks.
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
It's easy enough to get a shared template & apply it. You don't even have to download-then-upload or copy-and-paste, a publicly-accessible url works.
Diving deeper, it has enough powerful capabilities to make the life of a platform / DevOps engineer bearable.
However, you need equally deep knowledge to troubleshoot issues, when they inevitably pop up. This is the same for all IaC technologies, as they are additional abstraction layers on top of the native API provided by the cloud providers.
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
