Can I use pre-trained models instead of training from scratch?

Yes, Flow Nexus Neural includes a template marketplace where you can list, download, and deploy pre-trained models for various tasks like vision or NLP.

Is my data secure during distributed training?

Yes, the skill supports federated learning configurations where data stays on local worker nodes and only model gradients are shared with the aggregator.

What neural network architectures are supported?

The skill supports a wide range of architectures including Feedforward, LSTM, GAN, Autoencoder, and Transformer models.

Does this skill require my own local GPU?

No, training and inference are performed in distributed E2B sandboxes, leveraging cloud-based compute resources rather than your local hardware.

How do I monitor the progress of a training job?

You can use the training_status or cluster_status commands to track real-time metrics such as loss, accuracy, and estimated completion time.

Flow Nexus Neural Networks

Name: Flow Nexus Neural Networks
Author: Activer007

byActiver007

Data Science & ML

Builds, trains, and deploys distributed neural networks within secure E2B sandbox environments using Flow Nexus.

About

Flow Nexus Neural Networks empowers developers to manage complex machine learning lifecycles directly within Claude. By leveraging E2B sandboxes and the Flow Nexus MCP server, users can architect diverse models ranging from simple feedforward networks to advanced transformers. The skill supports single-node training, distributed cluster management for large-scale tasks, and a marketplace for pre-trained templates, making it a comprehensive solution for integrating high-performance deep learning capabilities into automated AI workflows.

Key Features

Integrated performance benchmarking and validation workflows
Support for Transformer, LSTM, GAN, and Feedforward architectures
0 GitHub stars
Distributed training across multiple E2B sandbox clusters
Pre-trained model marketplace for rapid deployment and fine-tuning
Advanced federated learning and decentralized training capabilities

Use Cases

Setting up distributed training clusters for large-scale datasets with data privacy
Deploying and fine-tuning marketplace templates for specialized business tasks
Training custom sentiment analysis or image classification models from scratch

About

Key Features

Integrated performance benchmarking and validation workflows
Support for Transformer, LSTM, GAN, and Feedforward architectures
0 GitHub stars
Distributed training across multiple E2B sandbox clusters
Pre-trained model marketplace for rapid deployment and fine-tuning
Advanced federated learning and decentralized training capabilities

Use Cases

Setting up distributed training clusters for large-scale datasets with data privacy
Deploying and fine-tuning marketplace templates for specialized business tasks
Training custom sentiment analysis or image classification models from scratch