Echo

Echo is a simulation framework designed to address the challenges of large-scale distributed training in machine learning. It focuses on tracing runtime training workloads, estimating collective communication, and accounting for computation slowdown due to interference. Echo achieves a low error rate in training step estimation, making it a valuable tool for managing massive ML clusters.

Category: Artificial Intelligence
Subcategory: Distributed Training Simulation
Tags: simulationdistributed trainingmachine learninglarge-scaleGPU
AI Type: Machine Learning
Programming Languages: Not specified
Frameworks/Libraries: Not specified
Application Areas: Large-scale machine learning training
Manufacturer Company: Not specified
Country: Not specified
Algorithms Used

Simulation-based algorithms

Model Architecture

Not specified

Datasets Used

Not specified

Performance Metrics

8% error in training step estimation

Deployment Options

Not specified

Cloud Based

No

On Premises

Yes

Features

Low error rate, efficient simulation, large-scale training management

Enterprise

Yes

Hardware Requirements

96-GPU H800 cluster

Supported Platforms

Not specified

Interoperability

Not specified

Security Features

Not specified

Compliance Standards

Not specified

Certifications

Not specified

Open Source

No

Source Code URL

http://Not specified

Documentation URL

http://Not specified

Community Support

Not specified

Contributors

Not specified

Training Data Size

Not specified

Inference Latency

Not specified

Energy Efficiency

Not specified

Explainability Features

Not specified

Ethical Considerations

Not specified

Known Limitations

Not specified

Industry Verticals

Technology, Research

Use Cases

Simulation of distributed training, ML cluster management

Customer Base

Not specified

Integration Options

Not specified

Scalability

High

Support Options

Not specified

SLA

Not specified

User Interface

Not specified

Multi-Language Support

No

Localization

Not specified

Pricing Model

Not specified

Trial Availability

No

Partner Ecosystem

Not specified

Patent Information

Not specified

Regulatory Compliance

Not specified

Version

Not specified

Website URL

http://Not specified

Service Type

Not specified

Has API

No

API Details

Not specified

Business Model

Not specified

Price

0.00

Currency

Not specified

License Type

Not specified

Release Date

01/01/1970

Last Update Date

01/01/1970

Contact Email

Not specified

Contact Phone

Not specified

Social Media Links

http://Not specified

Other Features

Not specified

Published

Yes