Inspur AI: System Management

Dynamically pool, disaggregate and allocate system resources for intensive deep learning applications, maintain oversight and make adjustments in real time.

AIStation Deep Learning Management

AIStation supports unified management and scheduling of AI computing resources to enhance resource and labor efficiency and accelerate AI application development.

Deep Learning Workflow

Allows rapid deployment and provides whole-process services like data pre-processing, parameter tuning, training monitoring, result analysis, etc.

Model Training Management

Hyper-parameter search to reduce training time. Real-time monitoring and visualization of training tasks and errors accelerates development.

Dynamic Resource Allocation

Real-time GPU scheduling to enhance resource utilization. Supports GPU sharing among users.

Easy-to-Use, Complete Deep Learning Workflow

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.

Efficient One-Click AI Training Environment Deployment

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.

Dynamic GPU Resource Allocation

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.

Unified Cluster and Resource Management

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.

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