agentic-cloud-neural

Distributed neural training across sandboxes is managed through Agentic Cloud E2B environments. You initiate a workflow by selecting a model architecture, dataset, and cluster configuration to deploy nodes and start training.

Distributed training supports multi-architecture models including feedforward, lstm, gan, and transformer. These architectures can be trained across managed clusters with resource governance and federated learning settings.

Model management includes deployment, inference, and template marketplace integration. You can publish trained neural network templates to the marketplace for rapid production deployment and cross-cluster reuse.

Federated learning enables privacy-preserving training across cross-cluster workflows. It supports scalable experimentation by coordinating distributed nodes without centralizing the underlying dataset.

To initialize a cluster in E2B environments, you need a chosen architecture, dataset, and cluster configuration. Concrete commands allow you to deploy nodes, start training, monitor progress, and manage resources.