Amazon Elastic InferenceAmazon
|
||||||
Related Products
|
||||||
About
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Sagemaker instances or Amazon ECS tasks, to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, PyTorch and ONNX models. Inference is the process of making predictions using a trained model. In deep learning applications, inference accounts for up to 90% of total operational costs for two reasons. Firstly, standalone GPU instances are typically designed for model training - not for inference. While training jobs batch process hundreds of data samples in parallel, inference jobs usually process a single input in real time, and thus consume a small amount of GPU compute. This makes standalone GPU inference cost-inefficient. On the other hand, standalone CPU instances are not specialized for matrix operations, and thus are often too slow for deep learning inference.
|
About
Distributed AI is a computing paradigm that bypasses the need to move vast amounts of data and provides the ability to analyze data at the source. Distributed AI APIs built by IBM Research is a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge computing environments. Each Distributed AI API addresses the challenges in enabling AI in distributed and edge environments with APIs. The Distributed AI APIs do not focus on the basic requirements of creating and deploying AI pipelines, for example, model training and model serving. You would use your favorite open-source packages such as TensorFlow or PyTorch. Then, you can containerize your application, including the AI pipeline, and deploy these containers at the distributed locations. In many cases, it’s useful to use a container orchestrator such as Kubernetes or OpenShift operators to automate the deployment process.
|
|||||
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
|||||
Audience
IT teams that need an advanced Infrastructure as a Service solution
|
Audience
Developers interested in a solution offering data and AI algorithms to support their AI applications
|
|||||
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
|
|||||
API
Offers API
Not Supported
|
API
Offers API
Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
Pricing
No information available.
Free Version
Not Supported
Free Trial
Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
|
Training
Documentation
Supported
Webinars
Supported
Live Online
Not Supported
In Person
Supported
|
|||||
Company InformationAmazon
Founded: 2006
United States
aws.amazon.com/machine-learning/elastic-inference/
|
Company InformationIBM
United States
developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
|
|||||
Categories |
Categories |
|||||
Integrations
PyTorch
Supported
TensorFlow
Supported
Amazon EC2
Supported
Amazon EC2 G4 Instances
Supported
Amazon Web Services (AWS)
Supported
Kubernetes
Not Supported
MXNet
Supported
Red Hat OpenShift
Not Supported
|
Integrations
PyTorch
Supported
TensorFlow
Supported
Amazon EC2
Not Supported
Amazon EC2 G4 Instances
Not Supported
Amazon Web Services (AWS)
Not Supported
Kubernetes
Supported
MXNet
Not Supported
Red Hat OpenShift
Supported
|
|||||
|
|
|