Average Ratings 0 Ratings
Average Ratings 0 Ratings
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.
Description
TorchMetrics comprises over 90 implementations of metrics designed for PyTorch, along with a user-friendly API that allows for the creation of custom metrics. It provides a consistent interface that enhances reproducibility while minimizing redundant code. The library is suitable for distributed training and has undergone thorough testing to ensure reliability. It features automatic batch accumulation and seamless synchronization across multiple devices. You can integrate TorchMetrics into any PyTorch model or utilize it within PyTorch Lightning for added advantages, ensuring that your data aligns with the same device as your metrics at all times. Additionally, you can directly log Metric objects in Lightning, further reducing boilerplate code. Much like torch.nn, the majority of metrics are available in both class-based and functional formats. The functional versions consist of straightforward Python functions that accept torch.tensors as inputs and yield the corresponding metric as a torch.tensor output. Virtually all functional metrics come with an equivalent class-based metric, providing users with flexible options for implementation. This versatility allows developers to choose the approach that best fits their coding style and project requirements.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
PyTorch
Yes
GitHub
Yes
Lightning AI
No
NVIDIA NeMo
Yes
NVIDIA RAPIDS
Yes
NumPy
Yes
TensorFlow
Yes
Integrations
PyTorch
Yes
GitHub
No
Lightning AI
Yes
NVIDIA NeMo
No
NVIDIA RAPIDS
No
NumPy
No
TensorFlow
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
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
Yes
Mac
Yes
Linux
Yes
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
Yes
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
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/flare
Vendor Details
Company Name
TorchMetrics
Country
United States
Website
torchmetrics.readthedocs.io/en/stable/
Product Features
Product Features
Application Development
Access Controls/Permissions
No
Code Assistance
No
Code Refactoring
No
Collaboration Tools
No
Compatibility Testing
No
Data Modeling
No
Debugging
No
Deployment Management
No
Graphical User Interface
No
Mobile Development
No
No-Code
No
Reporting/Analytics
No
Software Development
No
Source Control
No
Testing Management
No
Version Control
No
Web App Development
No