Fabric for Deep Learning (FfDL) 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.

Integrations

API:
Yes, Fabric for Deep Learning (FfDL) has an API

Reviews

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Company Details

Company:
IBM
Year Founded:
1911
Headquarters:
United States
Website:
developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/

Media

Fabric for Deep Learning (FfDL) Screenshot 1
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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Webinars
Training Videos
Customer Support
Online Support

Fabric for Deep Learning (FfDL) Features and Options

Deep Learning Software

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

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