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features
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support

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Description

EcoStruxure Foxboro DCS, a progressive advancement from Foxboro Evo, represents a cutting-edge series of resilient and dependable control systems designed to unify essential data and enhance the workforce's effectiveness, thereby guaranteeing uninterrupted and efficient plant operations. Tailored real-time accounting frameworks are integrated to evaluate and manage the financial implications of each process point. The family of Foxboro DCS components, characterized by their fault tolerance and high availability, efficiently gathers, processes, and transmits critical information throughout the entire facility. Designed with adaptability and scalability at its core, the Foxboro DCS provides various controllers and I/O options to meet diverse cost, spatial, and functionality needs. Furthermore, the system includes advanced, multi-functional workstations and servers that are both flexible and robust, offering a range of choices suited for distinct operational settings and requirements within the plant. This comprehensive design ensures that facilities can operate smoothly while also adapting to future technological advancements.

Description

Deep learning frameworks like TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have significantly enhanced the accessibility of deep learning by simplifying the design, training, and application of deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) offers a standardized method for deploying these deep-learning frameworks as a service on Kubernetes, ensuring smooth operation. The architecture of FfDL is built on microservices, which minimizes the interdependence between components, promotes simplicity, and maintains a stateless nature for each component. This design choice also helps to isolate failures, allowing for independent development, testing, deployment, scaling, and upgrading of each element. By harnessing the capabilities of Kubernetes, FfDL delivers a highly scalable, resilient, and fault-tolerant environment for deep learning tasks. Additionally, the platform incorporates a distribution and orchestration layer that enables efficient learning from large datasets across multiple compute nodes within a manageable timeframe. This comprehensive approach ensures that deep learning projects can be executed with both efficiency and reliability.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Caffe No 
Kubernetes No 
PyTorch No 
TensorFlow No 
Torch No 

Integrations

Caffe Yes 
Kubernetes Yes 
PyTorch Yes 
TensorFlow Yes 
Torch Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Schneider Electric

Founded

1836

Country

France

Website

www.se.com/us/en/work/products/industrial-automation-control/foxboro-dcs/

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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