Does this require my own GPU infrastructure?

No, the skill uses E2B sandboxes to provide the necessary environment for training and inference, though specific tiers offer different resource levels.

What neural network architectures are supported?

Flow Nexus supports a variety of architectures including Feedforward, LSTM, GAN, Autoencoder, and Transformer models.

How do I monitor the progress of a training job?

You can use the training status and cluster status commands to view real-time metrics such as loss, accuracy, and estimated completion time.

Are there pre-trained models available?

Yes, you can browse the Template Marketplace to find and deploy pre-trained models for image classification, sentiment analysis, and more.

Can I train models across multiple nodes?

Yes, the skill allows you to initialize clusters and deploy dedicated worker nodes, parameter servers, and aggregators for distributed training.

Flow Nexus Neural Networks

Name: Flow Nexus Neural Networks
Author: ricable

byricable

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데이터 과학 및 ML

Trains and deploys complex neural network architectures within distributed E2B sandbox environments using Flow Nexus.

소개

Flow Nexus Neural Networks is a comprehensive machine learning skill for Claude Code that enables developers to build, train, and manage deep learning models directly from their development environment. Leveraging E2B sandboxes for secure and isolated execution, it supports a wide array of architectures including Transformers, LSTMs, and GANs. The skill facilitates both single-node training for lightweight tasks and large-scale distributed training across compute clusters, complete with model versioning, performance benchmarking, and a marketplace for deploying pre-trained templates.

주요 기능

Support for multiple architectures including Transformers, CNNs, LSTMs, and GANs.
Integrated marketplace for deploying and sharing pre-trained model templates across categories.
1 GitHub stars
Distributed training across E2B sandbox clusters with configurable topologies (mesh, ring, star).
Comprehensive model management with performance benchmarking and validation workflows.
Federated learning capabilities for training on decentralized datasets.

사용 사례

Developing time-series forecasting models with LSTMs for financial or IoT data.
Training custom NLP sentiment classifiers using fine-tuned Transformer architectures.
Scaling heavy computer vision training tasks across distributed worker nodes and parameter servers.

소개

주요 기능

Support for multiple architectures including Transformers, CNNs, LSTMs, and GANs.
Integrated marketplace for deploying and sharing pre-trained model templates across categories.
1 GitHub stars
Distributed training across E2B sandbox clusters with configurable topologies (mesh, ring, star).
Comprehensive model management with performance benchmarking and validation workflows.
Federated learning capabilities for training on decentralized datasets.

사용 사례

Developing time-series forecasting models with LSTMs for financial or IoT data.
Training custom NLP sentiment classifiers using fine-tuned Transformer architectures.
Scaling heavy computer vision training tasks across distributed worker nodes and parameter servers.