Katib: A Distributed General AutoML Platform on Kubernetes
Automatic Machine Learning (AutoML) is a powerful mechanism to design and tune models. We present Katib, a scalable Kubernetes-native general AutoML platform that can support a range of AutoML algorithms including both hyper-parameter tuning and neural architecture search. The system is divided into separate components, encapsulated as micro-services. Each micro-service operates within a Kubernetes pod and communicates with others via well-defined APIs, thus allowing flexible management and scalable deployment at a minimal cost. Together with a powerful user interface, Katib provides a universal platform for researchers as well as enterprises to try, compare and deploy their AutoML algorithms, on any Kubernetes platform.
Code (1)
Tasks
AutoMLHyperparameter OptimizationManagementNeural Architecture SearchSimilar Papers 제목 키워드 기반
Multi-tenant Kubernetes Use Cases for AI, Secure Computing and Data Services, and More
Kubernetes, as a container orchestration engine, has been widely used in cloud-native ecosystems for several years. In supercomputing ecosystems, especially where bare-metal performance for compute and network devices ar…
A Scalable and Cloud-Native Hyperparameter Tuning System
In this paper, we introduce Katib: a scalable, cloud-native, and production-ready hyperparameter tuning system that is agnostic of the underlying machine learning framework. Though there are multiple hyperparameter tunin…
Urdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation
Automatic Handwritten Text Recognition (HTR) is inherently a challenging task, and its complexity is further increased when dealing with cursive scripts. Although significant efforts have been made on various cursive scr…
Handwritten Text RecognitionHandwriting RecognitionResource-Aware Pareto-Optimal Automated Machine Learning Platform
In this study, we introduce a novel platform Resource-Aware AutoML (RA-AutoML) which enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives, as well as resource and …
AutoMLBayesian OptimizationBIG-bench Machine LearningNeural Architecture SearchBlazeAIoT: A Modular Multi-Layer Platform for Real-Time Distributed Robotics Across Edge, Fog, and Cloud Infrastructures
The increasing complexity of distributed robotics has driven the need for platforms that seamlessly integrate edge, fog, and cloud computing layers while meeting strict real-time constraints. This paper introduces BlazeA…