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Ratings and Reviews 0 Ratings
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What is Fabric for Deep Learning (FfDL)?
Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have greatly improved the ease with which deep learning models can be designed, trained, and utilized. Fabric for Deep Learning (FfDL, pronounced "fiddle") provides a unified approach for deploying these deep-learning frameworks as a service on Kubernetes, facilitating seamless functionality. The FfDL architecture is constructed using microservices, which reduces the reliance between components, enhances simplicity, and ensures that each component operates in a stateless manner. This architectural choice is advantageous as it allows failures to be contained and promotes independent development, testing, deployment, scaling, and updating of each service. By leveraging Kubernetes' capabilities, FfDL creates an environment that is highly scalable, resilient, and capable of withstanding faults during deep learning operations. Furthermore, the platform includes a robust distribution and orchestration layer that enables efficient processing of extensive datasets across several compute nodes within a reasonable time frame. Consequently, this thorough strategy guarantees that deep learning initiatives can be carried out with both effectiveness and dependability, paving the way for innovative advancements in the field.
What is AWS Fault Injection Service?
Recognize the limitations in performance and potential weaknesses that traditional software testing may overlook. It is crucial to set definitive guidelines for stopping an experiment or returning to the pre-experiment state. Conduct tests rapidly by utilizing predefined scenarios from the extensive library provided by the AWS Fault Injection Service (FIS). By simulating authentic failure conditions, teams can gain deeper understanding of how different resources may perform under strain. As part of the AWS Resilience Hub, FIS serves as a robust tool for executing fault injection tests to improve application performance, visibility, and durability. The service simplifies the process of setting up and conducting controlled fault injection tests across various AWS services, which helps teams cultivate confidence in how their applications behave. Additionally, FIS incorporates vital safety features that allow teams to run experiments in production environments with safeguards in place, such as the automatic ability to halt or revert the experiment based on specific pre-established criteria, thereby enhancing overall safety during testing. This functionality equips development teams with the knowledge they need to navigate their applications in high-pressure situations and prepares them for unforeseen challenges. Ultimately, the use of FIS not only improves resilience but also fosters a more proactive approach to application performance management.
Integrations Supported
AWS Resilience Hub
Amazon Web Services (AWS)
Caffe
Kubernetes
PyTorch
TensorFlow
Torch
Integrations Supported
AWS Resilience Hub
Amazon Web Services (AWS)
Caffe
Kubernetes
PyTorch
TensorFlow
Torch
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
$0.10 per action-minute
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
IBM
Date Founded
1911
Company Location
United States
Company Website
developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/
Company Facts
Organization Name
Amazon
Company Location
United States
Company Website
aws.amazon.com/fis/
Categories and Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization