Amazon Elastic Kubernetes Service (Amazon EKS) is a managed container service to run and scale Kubernetes applications in the cloud or on-premises, available on AWS or on-premise through Amazon EKS Anywhere.
$0.10
per month
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.
N/A
Pricing
Amazon Elastic Kubernetes Service (EKS)
TensorFlow
Editions & Modules
Amazon EKS Cluster
$.10
per hour of each cluster created
No answers on this topic
Offerings
Pricing Offerings
Amazon EKS
TensorFlow
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
—
—
More Pricing Information
Community Pulse
Amazon Elastic Kubernetes Service (EKS)
TensorFlow
Features
Amazon Elastic Kubernetes Service (EKS)
TensorFlow
Container Management
Comparison of Container Management features of Product A and Product B
It is well suited when you want to have a Kubernetes cluster in AWS Cloud and want to avoid all the management overhead of maintaining your own cluster in terms of the control plane. EKS seems to be lacking in features when compared with AKS and GKE. Backups, service mesh, and monitoring have a lot of room for improvements.
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.
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.
It feels like AWS is behind the EKS race, the only advantage I'm able to see right now is the support of IPv6, however, trying to promote AWS alternatives that are different from the market and more like a vendor locking solutions like ECS/Fargate have kept AWS behind and focusing on the wrong things. EKS needs to really improve its integration with the Kubernetes ecosystem and have an enterprise solution for monitoring, backups, and service mesh.
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
