AI Training and Inference

The process of utilizing EMC for AI training and AI inference is as follows:

1. Resource Preparation: EMC integrates idle GPUs from abandoned Ethereum (ETH) miners and Filecoin (FIL) miners to build a distributed GPU computing network. Before using EMC, it is necessary to ensure that the computing nodes have the appropriate hardware and the capability to connect to the EMC network.

2. Task Scheduling: The EMC protocol handles task scheduling and task distribution. During AI training, the task scheduling module divides the tasks into appropriate computing units and distributes them to participating computing nodes in the EMC network. This ensures that tasks can be efficiently and in parallel executed across the distributed GPU computing nodes.

3. AI Model Training: When using EMC for AI model training, the task scheduling module distributes the training tasks to available computing nodes. These nodes utilize their GPU computing power to execute the training tasks, performing backpropagation and parameter optimization using datasets, ultimately training high-performance AI models.

4. AI Inference: Once the AI models are trained, EMC can be used for AI inference. Inference tasks involve passing input data to the trained models and generating corresponding output results. The task scheduling module distributes the inference tasks to computing nodes, which leverage their GPU computing power to perform real-time processing of input data and generate inference results.

By distributing AI training and inference tasks to the distributed GPU computing network of EMC, task processing speed and computational efficiency can be accelerated. EMC's distributed architecture provides greater computational power and flexibility, enabling more efficient and scalable AI model training and inference.

It is important to note that utilizing EMC for AI training and inference requires appropriate technical configurations and resource management to ensure smooth task execution. Additionally, EMC provides economic incentives to encourage participation and contribution from computing nodes, further promoting the development and application of AI training and inference.