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bump-base-image

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17.4k|4.3k|Updated Mar 21, 2019
Search Tags:#release management#docker#pytorch#ci#functional tests#ngc#golden values
One-click install
npx skills add https://github.com/NVIDIA/Megatron-LM --skill bump-base-image-nvidia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bump-base-image
Source: https://github.com/NVIDIA/Megatron-LM/tree/main/skills/bump-base-image
Command: npx skills add https://github.com/NVIDIA/Megatron-LM --skill bump-base-image-nvidia

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents CI breakage when upgrading Megatron-LM’s NVIDIA PyTorch container base image by ensuring both GitHub and GitLab CI pins are updated together and that downstream functional-test expectations stay consistent.

Core Features & Use Cases

  • Dual-pin container bump: Updates the GitHub CI tag in docker/.ngc_version.dev and the GitLab CI BASE_IMAGE tag in .gitlab/stages/01.build.yml for both amd64 and arm64, avoiding the common “one CI updated, the other still old” failure mode.
  • CI validation loop guidance: Recommends re-running functional tests, handling golden-value drift, and responding appropriately to hangs/OOMs or true regressions instead of conflating them with the container change.
  • Recipe scope handling: Provides the exact mr-broken / mr-github-broken scope mapping to keep broken cases runnable without blocking PR merges.

Quick Start

Use the bump-base-image skill to upgrade the CI container tag (for example from the current nvcr.io/nvidia/pytorch:<YY.MM>-py3 to a new <YY.MM>-py3) and then refresh golden values if functional metrics drift.

Frequently Asked Questions about bump-base-image

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

FAQPage Schema
How do I bump the PyTorch base image in Megatron-LM CI without breaking tests?▼

To bump the PyTorch base image without breaking tests, update the GitHub CI tag in docker/.ngc_version.dev and the GitLab CI BASE_IMAGE in .gitlab/stages/01.build.yml for amd64 and arm64, then re-run functional tests and refresh golden values on drift.

Why does my CI break after upgrading the NVIDIA PyTorch container?▼

CI breaks after upgrading the NVIDIA PyTorch container because GitHub and GitLab image pins often fall out of sync. Updating both docker/.ngc_version.dev and .gitlab/stages/01.build.yml BASE_IMAGE entries together prevents this failure mode.

How do I isolate true regressions when functional tests fail after a base image bump?▼

To isolate true regressions after a base image bump, switch test scopes to mr-broken or mr-github-broken variants. This keeps broken cases runnable without blocking PR merges while distinguishing true regressions from container changes.

Does bumping the nvcr.io/nvidia/pytorch tag affect both amd64 and arm64 CI matrix builds?▼

Bumping the nvcr.io/nvidia/pytorch tag affects both amd64 and arm64 CI matrix builds. You must update the BASE_IMAGE entries for both architectures in .gitlab/stages/01.build.yml to ensure architecture-specific CI matrix builds validate successfully.

When do I need to use mr-broken scopes for Megatron-LM CI maintenance?▼

You need to use mr-broken or mr-github-broken scopes for Megatron-LM CI maintenance when isolating true regressions after a container upgrade. This mapping keeps broken cases runnable without blocking PR merges during the validation loop.

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