🚴Isolated Environment Architecture

This section primarily draws from the design philosophies of Ollama and Anaconda. The isolated environment architecture is a core technical infrastructure designed specifically for Large Language Models (LLM) and Machine Learning (ML) applications. This architecture creates independent runtime spaces to ensure complete isolation of environments for different models and experiments, thereby enhancing system stability and reliability.

Dependency Management

In complex machine learning ecosystems, dependency management is crucial. Our architecture provides independent runtime environments for each project, effectively resolving version conflict issues. For example, when one project requires TensorFlow 2.0 while another needs PyTorch 1.9, the system can perfectly maintain these two separate environments, ensuring smooth workflow progression.

Environment Independence

Environment independence is the cornerstone of this architecture. Through strict environment isolation mechanisms, each project runs in its dedicated space, effectively preventing mutual interference. This not only enhances system security but also provides an ideal workspace for development and testing. Researchers can freely conduct model training and debugging in independent environments while maintaining production environment stability.

Environment Migration and Collaboration

Our architecture provides advanced environment migration and collaboration capabilities. Research teams can easily export complete environment configuration templates, enabling rapid environment replication and deployment. This greatly improves team collaboration efficiency, ensuring all members work under identical environment configurations, effectively avoiding the ``it works on my machine'' problem.

Version Control System

The version control system employs precise dependency management mechanisms and integrates mainstream package management tools. The system supports automatic dependency resolution and provides version locking functionality, ensuring reproducibility in model training processes. Through this approach, researchers can ensure the reproduction of identical experimental results at different points in time.

Multi-Platform Compatibility

Our architecture achieves exceptional cross-platform compatibility. Through unified configuration standards and environment management strategies, it ensures consistent model performance across different operating systems. Whether in a Windows development environment or on Linux production servers, models maintain the same performance levels and operational effects, greatly simplifying the deployment process.

Docker Container Image

This project adopts the environment management philosophy of Ollama and Anaconda in its Docker container image design. As a key technical component of Isolated Spaces, Docker container images play a central role in environment management for LLM and machine learning models. Through integrating applications and their dependencies into lightweight, portable containers, we have achieved environment standardization and consistency. By integrating Ollama's professional environment management architecture with Anaconda's efficient package management system, we have built independent runtime environments for each LLM model, effectively avoiding dependency conflicts between models. From a technical implementation perspective, Docker images provide research teams with a comprehensive environment encapsulation solution, enabling rapid deployment, flexible scaling, precise version control, and collaborative development, significantly enhancing the practical value and applicability of Isolated Spaces.