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What is Rector?

Rector is an advanced PHP tool designed to facilitate immediate upgrades and automate refactoring for any PHP project, making the process of upgrading PHP versions, migrating in-house frameworks, and improving code quality much more efficient, which ultimately allows you to deliver features faster than your rivals. When leveraged by a skilled user, Rector can drastically reduce the time needed for various tasks; for example, a project upgrade from PHP 8.0 to 8.3, which may usually take three months, can be completed in just three days with Rector’s capabilities. To get started, you can either explore the extensive documentation available or speed up your learning curve by collaborating with our upgrade team, who have successfully supported over 50 companies in enhancing their PHP code quality while reducing technical debt. Rector takes your code quality to new heights, as its comprehensive knowledge is organized into easily executable rules and sets tailored to your code base. Once you progress past PHP 8.0 and implement a robust Rector CI setup, the time required to upgrade to the latest PHP version shrinks to just a few hours, allowing you to remain competitive in a rapidly changing technological environment. By adopting Rector, not only do you improve your codebase, but you also equip your team to adapt quickly to future innovations and challenges, ensuring sustained growth and efficiency. This adaptability ensures that your development processes are not just reactive but also proactive in anticipating the needs of upcoming projects.

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

Caffe
Kubernetes
Laravel
PHP
PyTorch
Symfony
TensorFlow
Torch

Integrations Supported

Caffe
Kubernetes
Laravel
PHP
PyTorch
Symfony
TensorFlow
Torch

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
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

Rector

Date Founded

2017

Company Location

Czech Republic

Company Website

getrector.com

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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