Node Lifecycle

From Activation to Serving Users

Cortensor's decentralized AI network relies on a structured and well-defined node lifecycle that ensures the stability, quality, and efficiency of AI inference services. This lifecycle begins with a node's entry into the network and progresses through stages of network assignments and user service.

1. Node Activation

When a node operator brings their node online, the first step is to signal activation to the Cortensor network. This activation notifies the network that the node is ready to serve both network needs and user requests. Upon receiving this activation signal, the network begins to assess the node's readiness through a series of tasks designed to validate its capabilities and ensure it meets the required standards.

2. Network Assignment (PoI and PoUW)

Once activated, the node undergoes a series of Proof of Inference (PoI) and Proof of Useful Work (PoUW) tasks. These tasks are crucial for maintaining the quality and reliability of the network. The assignment process involves three key types of collaboration with other nodes:

1. Creation of Workable Blocks or Transactions:

   • A randomly selected miner (Type 1) is tasked with creating a virtual block or transaction that other miners will collaborate on. This block serves as the foundation for subsequent tasks.

2. Prompt and Question Generation:

   • A second type of miner (Type 2), also randomly selected, generates detailed prompts and questions based on the initial block. These prompts are designed to guide the AI inference tasks and are agreed upon by all network nodes. The prompts are then submitted to the blockchain.

3. Completion and Answer Generation:

   • Multiple miners (Type 3) are then asked to generate completions based on the prompts provided by Type 2 miners. These miners must submit their outputs within a specified timeframe. Failure to meet the deadline results in penalties and a reduction in their reputation score.

This structured process ensures that nodes are not only capable of performing AI tasks but are also contributing to the network's overall health and quality control.

3. Transition to Ephemeral Node Status

After successfully completing a series of network-assigned tasks, the node transitions to an "ephemeral node" status. In this state, the node is ready to serve user requests based on specific requirements. Ephemeral nodes are designed to handle short-term tasks, such as AI inference for chatbots or simple clarifications, which are tied to user-created sessions.

• User Sessions:

  • Users create sessions by depositing a certain amount of ETH or tokens. These sessions allow users to subscribe to AI services, specifying the model, computation, and memory requirements. Users can choose between public ephemeral nodes, which offer guaranteed metrics and performance based on reputation, or reserved nodes that prioritize privacy and dedicated resources.

• Capacity Planning:

  • The pre-deposit mechanism for user sessions provides economic security for node operators by allowing them to predict network capacity needs and plan their operations accordingly. The total number of session deposits correlates with the network capacity required to support all user requests.

4. Ongoing Network Assignments and Quality Control

Even after becoming an ephemeral node, the node continues to receive PoI and PoUW tasks as part of ongoing network sampling. This continuous quality control process ensures that nodes maintain high standards and remain reliable for user requests. If a node fails to complete these tasks, its reputation score will be penalized, which can impact future network assignments and user payments.

5. Node Deactivation

Node operators can temporarily deactivate their nodes for maintenance or other reasons without affecting their reputation. By opting out of network assignments and PoI/PoUW tasks through a simple CLI command, they avoid penalties and can rejoin the network when ready.

Conclusion

Cortensor's node lifecycle—from activation and network assignments to serving user requests—ensures a robust and reliable decentralized AI network. The structured approach, combining gamified quality control and dynamic role allocation, makes Cortensor unique and predictable in delivering AI inference services. By adhering to this lifecycle, node operators can maximize their contributions to the network while earning rewards and maintaining a strong reputation.