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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.

Description

NVIDIA FLARE, which stands for Federated Learning Application Runtime Environment, is a versatile, open-source SDK designed to enhance federated learning across various sectors, such as healthcare, finance, and the automotive industry. This platform enables secure and privacy-focused AI model training by allowing different parties to collaboratively develop models without the need to share sensitive raw data. Supporting a range of machine learning frameworks—including PyTorch, TensorFlow, RAPIDS, and XGBoost—FLARE seamlessly integrates into existing processes. Its modular architecture not only fosters customization but also ensures scalability, accommodating both horizontal and vertical federated learning methods. This SDK is particularly well-suited for applications that demand data privacy and adherence to regulations, including fields like medical imaging and financial analytics. Users can conveniently access and download FLARE through the NVIDIA NVFlare repository on GitHub and PyPi, making it readily available for implementation in diverse projects. Overall, FLARE represents a significant advancement in the pursuit of privacy-preserving AI solutions.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

PyTorch Yes 
TensorFlow Yes 
Caffe Yes 
GitHub No 
Kubernetes Yes 
NVIDIA NeMo No 
NVIDIA RAPIDS No 
NumPy No 
Torch Yes 

Integrations

PyTorch Yes 
TensorFlow Yes 
Caffe No 
GitHub Yes 
Kubernetes No 
NVIDIA NeMo Yes 
NVIDIA RAPIDS Yes 
NumPy Yes 
Torch No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

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 Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

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

Customer Support

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

Types of Training

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

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

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

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/flare

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 

Product Features

Alternatives

Alternatives