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What is Edka?
Edka simplifies the creation of a fully operational Platform as a Service (PaaS) by utilizing standard cloud virtual machines and Kubernetes, dramatically reducing the manual effort required for application management on Kubernetes through its provision of preconfigured open-source add-ons that effectively convert a Kubernetes cluster into a robust PaaS environment.
To optimize Kubernetes management, Edka structures its operations into several distinct layers:
Layer 1: Cluster provisioning – An intuitive interface that enables users to create a k3s-based cluster with a single click and default configurations.
Layer 2: Add-ons – A straightforward one-click deployment option for critical components such as metrics-server, cert-manager, and various operators, all preconfigured for compatibility with Hetzner, eliminating the need for further setup.
Layer 3: Applications – User-friendly interfaces designed with minimal configurations specifically for applications that depend on the aforementioned add-ons.
Layer 4: Deployments – Edka guarantees automatic updates to deployments in line with semantic versioning standards, providing features like instantaneous rollbacks, autoscaling, persistent volume management, secret/environment imports, and rapid public accessibility for applications. This organized approach not only enhances operational efficiency but also empowers developers to concentrate on application development rather than infrastructure management, ultimately fostering innovation and productivity.
What is Bright Cluster Manager?
Bright Cluster Manager provides a diverse array of machine learning frameworks, such as Torch and TensorFlow, to streamline your deep learning endeavors. In addition to these frameworks, Bright features some of the most widely used machine learning libraries, which facilitate dataset access, including MLPython, NVIDIA's cuDNN, the Deep Learning GPU Training System (DIGITS), and CaffeOnSpark, a Spark package designed for deep learning applications. The platform simplifies the process of locating, configuring, and deploying essential components required to operate these libraries and frameworks effectively. With over 400MB of Python modules available, users can easily implement various machine learning packages. Moreover, Bright ensures that all necessary NVIDIA hardware drivers, as well as CUDA (a parallel computing platform API), CUB (CUDA building blocks), and NCCL (a library for collective communication routines), are included to support optimal performance. This comprehensive setup not only enhances usability but also allows for seamless integration with advanced computational resources.
Integrations Supported
Amazon Elastic Container Registry (ECR)
Docker
GitHub
Google Cloud Artifact Registry
Hetzner
Integrations Supported
Amazon Elastic Container Registry (ECR)
Docker
GitHub
Google Cloud Artifact Registry
Hetzner
API Availability
Has API
API Availability
Has API
Pricing Information
€0
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
Edka
Date Founded
2013
Company Location
Spain
Company Website
edka.io
Company Facts
Organization Name
NVIDIA
Date Founded
1993
Company Location
United States
Company Website
developer.nvidia.com/bright-cluster-manager
Categories and Features
DevOps
Approval Workflow
Dashboard
KPIs
Policy Management
Portfolio Management
Prioritization
Release Management
Timeline Management
Troubleshooting Reports
Categories and Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization