io.net is developing an enterprise-grade decentralized computing network that empowers machine learning engineers by providing access to distributed cloud clusters at a fraction of the cost of traditional centralized services. Our mission is to revolutionize the computing landscape, making compute resources as accessible and valuable as digital oil. By creating IO, the currency of compute, we aim to drive a technological industrial revolution with an ecosystem of products and services that transform compute into a readily available resource and asset.
Key Features of io.net
Batch Inference and Model Serving: By leveraging a distributed network of GPUs, io.net enables machine learning teams to efficiently perform batch inference and model serving. This process involves exporting the architecture and weights of trained models to a shared object-store, facilitating scalable and parallelized inference workflows.
Parallel Training: Addressing the limitations of CPU/GPU memory and sequential processing workflows, io.net uses distributed computing libraries to orchestrate and parallelize training jobs. This approach maximizes efficiency through data and model parallelism, allowing models to be trained across numerous distributed devices.
Parallel Hyperparameter Tuning: Hyperparameter tuning experiments benefit from io.net’s advanced distributed computing libraries, which optimize scheduling, checkpointing the best results, and simplifying the specification of search patterns. This makes hyperparameter tuning more efficient and effective.
Reinforcement Learning: io.net integrates an open-source reinforcement learning library to support highly distributed, production-level RL workloads. The system offers a straightforward set of APIs, enabling easy implementation and scaling of reinforcement learning tasks.
Through these features, io.net provides machine learning engineers with a robust, scalable, and cost-effective solution for managing and optimizing their computing needs.
