nemo-mbridge-perf-activation-recompute SKILL.md skillA
nemo-mbridge-perf-activation-recompute from nvidia/skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add nvidia/skills/nemo-mbridge-perf-activation-recompute@git:20260817.d36ff70Pins git:20260817.d36ff70 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- nvidia/skills
- Path
skills/nemo-mbridge-perf-activation-recompute/SKILL.md- Versions recorded
- 4
- Latest version
- git:20260817.d36ff70, committed Aug 17, 2026
- First version recorded
- May 29, 2026
- Size
- 18,915 bytes
- Repository stars
- 3,871
- License
- Apache-2.0
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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: - **Standard attention:** `core_attn` is the common first candidate. It is strongest when unfused attention materializes score/probability tensors. With Transformer Engine fused or Flash Attention, compare it against `[]` because those backends already rematerialize attention internals. …
Read the whole file at its exact version.
How to install
mdr add nvidia/skills/nemo-mbridge-perf-activation-recompute@sha256:03379723143dfcd3The line at the top of this page pins the label git:20260817.d36ff70, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_a25z4swu5g26bxlh)
2 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 4 versions of this file, between May 29, 2026 and Aug 17, 2026, about one every 27 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260817.d36ff70 latest | 2026-08-17 | d36ff70 | 18,915 B | A | view · diff |
| git:20260616.8491888 | 2026-06-16 | 8491888 | 9,197 B | A | view · diff |
| git:20260602.e695a83 | 2026-06-02 | e695a83 | 10,151 B | A | view · diff |
| git:20260529.7d99fda | 2026-05-29 | 7d99fda | 9,295 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (18915 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
nvidia/skills · 3,871 stars · license Apache-2.0 · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_a25z4swu5g26bxlh GET https://markdownregistry.com/api/v1/resolve?ref=nvidia/skills/nemo-mbridge-perf-activation-recompute GET https://markdownregistry.com/api/v1/blob/03379723143dfcd3cce4338c148fecc2aa515649ab85d3624e43a29b80f03e4e
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.