verl-deploy

Deploys Verl training services on NPU clusters with RocketMQ, SeaWeedFS, and OpenResty.

31|1|Updated Mar 28, 2026
One-click install
npx skills add https://github.com/Ascend/agent-skills --skill verl-deploy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: verl-deploy
Source: https://github.com/Ascend/agent-skills/tree/main/skills/verl-feature-deploy
Command: npx skills add https://github.com/Ascend/agent-skills --skill verl-deploy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires recipe, swanlab, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Verl Deploy enables one-click bootstrapping of Verl distributed training services on NPU clusters, automating environment validation, container provisioning, and monitoring configuration.

Core Features & Use Cases

  • One-click deployment of Verl on NPU clusters, including automatic Ray cluster setup and SwanLab monitoring.
  • Flexible acceleration with a 7-bit feature mask to enable/disable specific optimizations for Megatron-based models like Qwen3-8B.
  • Generated scripts and container-ready workflows that coordinate pre-checks, docker operations, and SwanLab integration.

Quick Start

Generate the deployment scripts with the provided generator and deploy Verl inside the container by running start_verl.sh.

Frequently Asked Questions about verl-deploy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy Verl for distributed training on an NPU cluster?

Deploy Verl on an NPU cluster using a one-click process that automates environment validation, Ray cluster setup, and SwanLab monitoring configuration. You generate deployment scripts with the provided generator and run start_verl.sh inside the container to launch training.

Can I use a 7-bit feature mask to enable optimizations for Megatron-based models like Qwen3-8B?

Yes, you can use a 7-bit feature mask to enable or disable specific acceleration optimizations for Megatron-based models like Qwen3-8B. This flexible masking allows fine-tuned control over distributed training performance on your provisioned containers.

What's the best way to configure Ray and SwanLab for distributed training?

The best way to configure Ray and SwanLab is through automated one-click deployment scripts that handle container orchestration and monitoring integration. The generated workflows coordinate pre-checks, docker operations, and SwanLab setup automatically.

Do I need specific environment checks before launching Verl distributed training?

Yes, you need environment checks before launching Verl. The deployment process enforces environment validation and pre-checks using provided scripts to ensure your NPU cluster is correctly provisioned and ready for container orchestration.

Does Verl deployment support container orchestration for distributed training tasks?

Yes, Verl deployment supports container orchestration through generated scripts and container-ready workflows. It coordinates docker operations, environment pre-checks, and SwanLab integration to ensure end-to-end deployment of distributed training on NPU clusters.

What are the limitations when deploying Verl on NPU clusters?

Deployment is specifically scoped to launching Verl on NPU clusters and configuring Megatron-based models. The provided scripts handle environment checks and container orchestration, but the 7-bit feature mask limits acceleration optimizations to supported model architectures like Qwen3-8B.