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 AIStation AI Deep Learning management system 
 As a deep learning management system developed by Inspur , DeeepEngine targets at deep learning training clusters and supports several types of deep learning frameworks . It can rapidly deploy training environment for deep learning , and comprehensively manage deep learning training tasks . 
 An efficient and convenient platform for the users is provided . The unified management , scheduling and monitoring of CPU and GPU resources during training runs on clusters can effectively raise utilization of computation resources and productivity . 
 Providing a pure deep learning workflow , convenient , easy to use 
 Deep learning training tasks contain a lot of steps . The AIStation provides full - process support , from data pre - processing , parameter tuning , allocation of computation resources , activation of training tasks , training monitoring to result analysis . 
 One - button to deploy a training environment for deep learning , rapid launching of training tasks and better efficiency 
 Deep learning has many frameworks and models , which often depend on different environments . When many of those are used in parallel , they will require a complicated development environment . The AIStation makes isolation and rapid deployment of resources and working environments an easy task . 
 Monitoring and visualization of training tasks , management of training progress and quality at any time , quickly detecting model problems . 
 Deep learning model trainings are time consuming . Training time last from several hours to days . Potential problems of a training run are often only after the completion of a run . To avoid such problems , the AIStation provides real - time monitoring and visualization of training tasks , log printing of cost function loss , training errors and testing errors of each step . 
 Comprehensive cluster monitoring and management , real - time control of utilization and operation of CPU / GPU resources 
 Deployment of deep learning environment 
 – Multiple deep learing frameworks : Caffe / TensorFlow / CNTK etc . 
 – Support various models : GoogleNet / VGG / ResNet etc . 
 – One - Key deployment of the distributed computing environment 
 – Application arrangement , rapid start of APPs 
 Management of deep learning training tasks 
 – Support for application templates , rapid submission of training tasks 
 – workflow , data pre - processing , model training , visualization 
 – Management of training tasks , observation of training progress and precision , parameter - tuning 
 Computing resources management and scheduling 
 – Monitoring of CPU / GPU running state and performance state 
 – Scheduling of GPU resources on demand , resource isolation 
 – Scheduling strategy : fair share , preemption , backfilling 
 – Recording of usage of CPU / GPU resources 
 – Statistics of usage of cluster resources , generate monthly reports 
 – Statistics and analysis of training tasks