Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
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
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