Network Volume
Network Volumes on the Greennode AI Platform provide a high-performance and scalable storage solution specifically designed to meet the storage and data management needs of AI projects. One of the standout features of Network Volumes is their ability to be accessed flexibly and easily from various components within the platform, including:
- Notebook Instances: Interactive working environments where data scientists and AI engineers can write, run, and debug code.
- Training Jobs: Model training jobs where AI models are trained on large datasets.
- Online Prediction: API endpoints that allow deployed models to make real-time predictions.
Why Use Network Volumes?
- Centralized Access: All components can access the same Network Volume, centralizing data management and avoiding redundancy.
- Easy Data Sharing: Sharing data between components becomes simpler and more efficient, fostering collaboration within projects.
- Improved Performance: Direct data access from Network Volumes reduces access time and accelerates data processing.
- Flexibility: Users can flexibly adjust access permissions and configure Network Volumes to meet specific requirements.
Typical Use Cases
- Training Data Storage: Store large datasets for training AI models.
- Model Storage: Store trained models for deployment and use.
- Data Sharing Among Team Members: Create a shared data repository for team members to access and share data.
- Integration with Other Tools: Integrate with external tools for complex data analysis tasks.
How It Works
- Create Network Volume: Users create a Network Volume and specify its capacity and storage location.
- Mount Volume: The Network Volume is mounted to instances (Notebook, Training Job,...) where data access is required.
- Access Data: Applications running on the instance can access data on the Network Volume as if it were a local disk.
- Unmount Volume: When no longer needed, the Network Volume can be unmounted from the instance.
Benefits of Using This Feature
- Increased Productivity: Reduces the time and effort required to manage data.
- Improved Performance: Accelerates data access and processing.
- Enhanced Security: Manages data access permissions tightly.
- Increased Flexibility: Easily scale storage capacity up or down.
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