How does this help with training performance?

It ensures your setup follows best practices for data sharding, gradient accumulation, and non-blocking communication to maximize hardware utilization.

Does this skill support both PyTorch and TensorFlow?

Yes, it provides implementation patterns and setup guidance for both PyTorch (DistributedDataParallel) and TensorFlow (MirroredStrategy/MultiWorkerMirroredStrategy).

Is it compatible with cloud providers like AWS or GCP?

Yes, the configurations generated are compatible with standard cloud-based GPU instances and containerized environments like Docker or Kubernetes.

Can it help with multi-node training clusters?

Yes, the skill assists in configuring the necessary communication backends, environment variables, and master-worker relationships required for multi-node setups.

Do I need prior knowledge of distributed computing?

While foundational ML knowledge is helpful, the skill simplifies the process by providing step-by-step guidance and generating validated boilerplate code for common distributed patterns.

Distributed Training Setup

Name: Distributed Training Setup
Author: jeremylongshore

byjeremylongshore

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983

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Data Science & ML

Configures multi-GPU and multi-node machine learning training environments using industry-standard frameworks and practices.

This skill automates the complex process of setting up distributed training architectures for large-scale machine learning models. It provides specialized guidance for implementing patterns like Distributed Data Parallel (DDP) and multi-node synchronization across PyTorch, TensorFlow, and other major ML frameworks. By generating production-ready configurations and validating environment setups, it ensures efficient resource utilization and minimizes the friction typically associated with scaling model training from a single device to distributed clusters.

Key Features

01983 GitHub stars

02Automated multi-GPU and multi-node environment configuration

03Production-ready implementation of DDP and MirroredStrategy patterns

04Step-by-step migration guidance from single-device to distributed training

05Validation of networking and communication backends

06Support for PyTorch, TensorFlow, and Horovod frameworks

Use Cases

01Scaling deep learning model training across multiple local or cloud GPUs

02Configuring multi-node clusters for large-scale language model pre-training

03Optimizing data loading and gradient synchronization for high-throughput training

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills distributed-training-setup

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01983 GitHub stars

02Automated multi-GPU and multi-node environment configuration

03Production-ready implementation of DDP and MirroredStrategy patterns

04Step-by-step migration guidance from single-device to distributed training

05Validation of networking and communication backends

06Support for PyTorch, TensorFlow, and Horovod frameworks

Use Cases

01Scaling deep learning model training across multiple local or cloud GPUs

02Configuring multi-node clusters for large-scale language model pre-training

03Optimizing data loading and gradient synchronization for high-throughput training

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills distributed-training-setup

For use in Claude.ai and ChatGPT

Download Skill