What is io.net ?
io.net, powered by Solana & Aptos, pioneers an enterprise-grade decentralized computing network facilitating machine learning engineers' access to distributed cloud clusters at a fraction of the cost compared to centralized services. Considered the "digital oil" of this generation, io.net fuels a technological revolution unseen before. The overarching vision of io.net is to establish IO as the currency of computing, fostering an ecosystem of products and services to enable computing access both as a resource and an asset.
What role does AI play in io.net?
Modern machine learning models heavily rely on parallel and distributed computing for optimal performance and scalability, especially in training and inference processes. Traditional cloud service providers fall short in meeting the demand from AI/ML companies, leading to limited availability, poor hardware choice, and high costs. io.net tackles these challenges by aggregating GPUs from underutilized sources such as independent data centers and crypto miners into a Decentralized Physical Infrastructure Network (DePIN). This network grants engineers access to significant on-demand computing power, customizable options, cost efficiency, and seamless implementation. io.net streamlines orchestration, scheduling, fault tolerance, and scaling, supporting various tasks including preprocessing, distributed training, hyperparameter tuning, reinforcement learning, and model serving. Designed primarily for Python workloads, io.net prioritizes AI/ML tasks, offering unparalleled flexibility and scalability.
What are the main use cases enabled by io.net?
- Batch Inference and Model Serving: Machine learning teams leverage io.net to parallelize inference tasks across a distributed network of GPUs, facilitating real-time processing of incoming data batches.
- Parallel Training: Overcoming limitations posed by CPU/GPU memory and sequential processing workflows, io.net employs distributed computing libraries to orchestrate and batch-train jobs across numerous devices using data and model parallelism.
- Parallel Hyperparameter Tuning: Capitalizing on the inherently parallel nature of hyperparameter tuning experiments, io.net optimizes scheduling, search patterns, and result checkpointing, enhancing efficiency and performance.
- Reinforcement Learning: Utilizing an open-source reinforcement learning library, io.net supports highly distributed RL workloads with production-level capabilities, offering a straightforward set of APIs for seamless integration and execution.
