flow-nexus-neural

Distributed neural network training orchestrates multiple compute nodes to parallelize model training across clusters. Flow Nexus enables this by provisioning E2B sandbox environments, coordinating training jobs across nodes, and managing checkpoints. You configure architecture type, training parameters like epochs and batch size, and cluster tier to scale training automatically.

Flow Nexus supports feedforward networks, LSTMs, GANs, autoencoders, and transformers. You specify architecture type and layer configuration in the training config, then submit to distributed clusters. Each architecture runs with your chosen training parameters and cluster tier.

Yes, model inference runs predictions from trained networks in E2B sandbox environments. After training completes, you load the model checkpoint and execute inference on new data. Flow Nexus handles sandbox provisioning and result streaming.

Yes, authenticated MCP server setup is required before training. This grants Flow Nexus access to sandbox orchestration, training CLI tooling, and cluster management. Setup is enforced as a technical requirement for all distributed training operations.

The template marketplace provides pre-trained neural network models for rapid deployment. Select a template matching your task, configure inference parameters, and deploy to E2B sandboxes. This skips architecture design and training time.

Specify architecture type (feedforward, LSTM, GAN, autoencoder, transformer), layer configuration, training parameters (epochs, batch size), and cluster tier (small, medium, large). Flow Nexus uses these to provision sandboxes, coordinate distributed workers, and manage training execution.