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What is TorchMetrics?

TorchMetrics offers a collection of over 90 performance metrics tailored for PyTorch, complemented by an intuitive API that enables users to craft custom metrics effortlessly. By providing a standardized interface, it significantly boosts reproducibility and reduces instances of code duplication. Furthermore, this library is well-suited for distributed training scenarios and has been rigorously tested to confirm its dependability. It includes features like automatic batch accumulation and smooth synchronization across various devices, ensuring seamless functionality. You can easily incorporate TorchMetrics into any PyTorch model or leverage it within PyTorch Lightning to gain additional benefits, all while ensuring that your metrics stay aligned with the same device as your data. Moreover, it's possible to log Metric objects directly within Lightning, which helps streamline your code and eliminate unnecessary boilerplate. Similar to torch.nn, most of the metrics are provided in both class and functional formats. The functional versions are simple Python functions that accept torch.tensors as input and return the respective metric as a torch.tensor output. Almost all functional metrics have a corresponding class-based version, allowing users to select the method that best suits their development style and project needs. This flexibility empowers developers to implement metrics in a way that aligns with their unique workflows and preferences. Furthermore, the extensive range of metrics available ensures that users can find the right tools to enhance their model evaluation and performance tracking.

What is Fabric for Deep Learning (FfDL)?

Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have greatly improved the ease with which deep learning models can be designed, trained, and utilized. Fabric for Deep Learning (FfDL, pronounced "fiddle") provides a unified approach for deploying these deep-learning frameworks as a service on Kubernetes, facilitating seamless functionality. The FfDL architecture is constructed using microservices, which reduces the reliance between components, enhances simplicity, and ensures that each component operates in a stateless manner. This architectural choice is advantageous as it allows failures to be contained and promotes independent development, testing, deployment, scaling, and updating of each service. By leveraging Kubernetes' capabilities, FfDL creates an environment that is highly scalable, resilient, and capable of withstanding faults during deep learning operations. Furthermore, the platform includes a robust distribution and orchestration layer that enables efficient processing of extensive datasets across several compute nodes within a reasonable time frame. Consequently, this thorough strategy guarantees that deep learning initiatives can be carried out with both effectiveness and dependability, paving the way for innovative advancements in the field.

Media

Media

Integrations Supported

PyTorch
Caffe
Kubernetes
Lightning AI
TensorFlow
Torch

Integrations Supported

PyTorch
Caffe
Kubernetes
Lightning AI
TensorFlow
Torch

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

TorchMetrics

Company Location

United States

Company Website

torchmetrics.readthedocs.io/en/stable/

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

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

Categories and Features

Application Development

Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development

Categories and 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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