Accelerate workloads with agile resource management.
Agile AI Development Platform
Simplify AI development with integrated AI dev environments for professional developers.
Fine-Grained GPU Scheduling
Enable multi-dimensional compute resource scheduling that improves utilization and productivity.
Efficient Dataset Management
Provides support for dataset pre-loading and cache, improving I/O efficiency.
AIStation Deep Learning Management
AIStation supports unified management and scheduling of AI computing resources to enhance resource efficiency and accelerate AI application development.
Allows rapid deployment and provides whole-process services like data pre-processing, parameter tuning, training monitoring, result analysis, etc.
Hyper-parameter search to reduce training time. Real-time monitoring and visualization of training tasks and errors accelerates development.
Real-time GPU scheduling to enhance resource utilization. Supports GPU sharing among users.
AIStation provides full-process support, including data pre-processing, parameter tuning, allocation of computation resources, activation of training tasks, monitoring for training tasks, and result analysis.
The numerous frameworks and models in different training tasks require a highly demanding development environment. AIStation makes isolation and rapid deployment of resources and dev environments easy.
AIStation can prioritize resources based on task criteria like size and runtime. The dynamic allocation of GPU resources enables optimal resource sharing and GPU efficiency.
AIStation supports comprehensive real-time monitoring for clusters, scheduling of training tasks, and timely detection of training problems to improve the reliability of clusters.
Teye Application Features Analysis
Teye is an Inspur-developed management tool used to analyze AI applications performance features of hardware and system resources running on GPU clusters, revealing the running features, hotspots and bottlenecks of these applications.
This allows Teye users get the most out of their applications computing potential on current platforms and subsequently provide an indication for algorithms optimization and improvement.
Performance Monitoring
Shows data distribution of each index, compares effects on application performance, collects runtime features and exposes hotspots and bottlenecks.
Feature Radar Chart
Based on an application’s requirements on the performance of major indexes, generates the radar chart of features, describes the performance features of the application and identifies critical indexes and performance bottlenecks.
Comparison Analysis
Through horizontal comparison of different model or algorithm features, creates an analysis of their performance to facilitate subsequent application model or algorithm optimization.
