nemo-mbridge-perf-megatron-fsdp

Enable Megatron FSDP overrides in Megatron-Bridge for distributed training.

Updated Apr 16, 2026
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nemo-mbridge-perf-megatron-fsdp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nemo-mbridge-perf-megatron-fsdp
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/nemo-mbridge-perf-megatron-fsdp
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nemo-mbridge-perf-megatron-fsdp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Megatron-Bridge users face memory constraints when training large models; enabling Megatron FSDP helps mitigate OOM errors and improves scalability.

Core Features & Use Cases

  • Enables Megatron FSDP override in Megatron-Bridge for memory-efficient data parallelism.
  • Provides code anchors, configuration guidance, pitfall notes, and verification steps to ensure correct setup.
  • Use Case: diagnose memory regressions and optimize checkpoint formats for FSDP-based training.

Quick Start

Run the Megatron FSDP override in Megatron-Bridge and perform a small validation to confirm the ckpt_format is fsdp_dtensor and the mutual exclusion with Torch FSDP2.

Frequently Asked Questions about nemo-mbridge-perf-megatron-fsdp

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

FAQPage Schema
How do I enable Megatron FSDP to mitigate OOM errors during distributed training?▼

To mitigate OOM errors during distributed training, enable the Megatron FSDP override in Megatron-Bridge to apply memory-efficient data parallelism. This provides configuration snippets and code anchors to optimize large model scalability.

What is the correct checkpoint format for Megatron FSDP in Megatron-Bridge?▼

The correct checkpoint format for Megatron FSDP is fsdp_dtensor. Verifying this checkpoint format is a required step to ensure compatibility and prevent memory regressions during FSDP-based training.

Can I use Megatron FSDP and Torch FSDP2 simultaneously for memory-efficient data parallelism?▼

No, you cannot use Megatron FSDP and Torch FSDP2 simultaneously because they are mutually exclusive. The override verifies compatibility rules to ensure only one data parallelism strategy is active.

How do I debug memory regressions when tuning data parallelism strategies with FSDP?▼

Debug memory regressions when tuning data parallelism strategies by applying the Megatron FSDP override and verifying checkpoint formats. This process diagnoses memory constraints and provides pitfall notes for FSDP-based training.

What are the limitations of using Megatron FSDP overrides in Megatron-Bridge?▼

Limitations of using Megatron FSDP overrides include strict mutual exclusion with Torch FSDP2 and a hard requirement for the checkpoint format to be fsdp_dtensor, which constrains compatible training configurations.