Is Syncfusion Essential JS 2 v22.4 Suitable for Real-Time AI Computer Monitoring?

[Alisa Aine]

Hi Syncfusion community!

I’ve been exploring tools to build a dashboard for monitoring the performance of my AI computer in real-time. I recently came across Syncfusion Essential JS 2 v22.4 and was wondering if it’s a good fit for this purpose.

Specifically, I’m looking to track metrics like GPU utilization, memory usage, and training progress of AI models. Since my AI computer processes large datasets and requires quick updates, I need dynamic charts, grids, and controls capable of handling real-time data streams efficiently. Does Essential JS 2 v22.4 provide the functionality needed for such applications?

Additionally, how efficient is its real-time data binding capability? Are the Chart or Grid components optimized for high-frequency updates without significant performance issues?

If anyone has used Syncfusion Essential JS 2 v22.4 for similar tasks involving an AI computer, I’d hear your experience. Are there any limitations, or should I consider specific configurations to maximize performance?

Looking forward to your insights and suggestions!

[Vasanthakumar K]

Hi Alisa Aine,

Greetings from Syncfusion support.

Query: I want to monitor metrics like GPU utilization, memory usage, and training progress of AI models using a grid-based dashboard. My system processes large datasets and requires real-time updates with grids and controls. Does Syncfusion Essential JS 2 v22.4 provide the required functionality? Additionally, how efficient is its real-time data binding capability? Is the Grid component optimized for high-frequency updates without significant performance issues?

Yes, Syncfusion Essential JS 2 v22.4 supports real-time data handling and offers optimized components for high-frequency updates in dynamic applications such as monitoring dashboards. Here's how it addresses your requirements:

1. Grid Component for Real-Time Updates

• The Syncfusion Grid is optimized for high-frequency data updates and can handle large datasets efficiently without significant performance degradation.
• It supports features like data paging, virtual scrolling, and real-time updates, making it suitable for dashboards that require quick updates.

2. Real-Time Data Binding Capabilities

• Local Data Binding:
• Ideal for small, static datasets.
• The dataSource should reference the complete model or collection of data, such as List<Product>.
• Documentation:

Local Data Binding for JavaScript
Local Data Binding for Core
Local Data Binding for MVC

• Remote Data Binding:
• Recommended for large or dynamically fetched datasets.
• The dataSource points to a URL or API endpoint that streams or provides updated data.
• Documentation:

Remote Data Binding for JavaScript
Remote Data Binding for Core
Remote Data Binding for MVC

3. Use Cases and Demos

• AI Integration Monitoring with Grid:
  AI Integration Grid Demo
• Live Stream Data Grid Demo Project:
  Live Streaming Stock Management Grid with Chart - Complete Project
• Live Stream Data Grid Working Stackblitz Example:
  Live Streaming Stock Management Grid Sample

4. Data Binding Choice Based on Application Needs

• If your dashboard needs to handle static datasets, use local data binding.
• For large, dynamic datasets, choose remote data binding for better performance and scalability.

By leveraging the Syncfusion Grid with appropriate data binding, you can build a robust dashboard to track metrics like GPU usage, memory stats, and training progress efficiently in real-time.

Regards,

Vasanthakumar K