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ease
features
design
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

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Caffe
Kubernetes
PyTorch
TensorFlow
Torch

Integrations

Caffe
Kubernetes
PyTorch
TensorFlow
Torch

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

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
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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