Menu

Individual skill

nemo-mbridge-perf-activation-recompute

0upvotes

Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute. Use for activation memory OOMs or regressions involving recompute_granularity, recompute_num_layers, recompute_modules, recompute_method, selective recompute, full recompute, or activation checkpointing.

skills/nemo-mbridge-perf-activation-recompute/SKILL.md

Read pinned source

Source commit 25340619336e89bcc40365c28bfe9e790430468e

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 2, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
3,900
Repository last updated
Nov 6, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Activation Recompute

Stable docs: @docs/training/activation-recomputation.md
Card: @skills/nemo-mbridge-perf-activation-recompute/card.yaml

<!-- Guidance refreshed: 2026-08-12. -->

Activation recompute (activation checkpointing) trades additional forward work during backward for lower retained-activation memory. The useful checkpoint boundary depends on the model architecture, attention backend, parallelism, and the tensor that actually drives the per-rank peak.

## Quick Decision Guide

1. Confirm the pressure is real allocation, not allocator fragmentation. Compare `max_memory_allocated()` with `max_memory_reserved()` on every rank.
2. Keep an explicit no-recompute control when the workload fits. Under selective granularity, `recompute_modules=[]` is valid and useful for this comparison.
3. Select the first boundary from the architecture and observed peak: