Cache and Skip Unchanged Steps
Re-run only what actually changed for speed.
Cache and Skip Unchanged Steps is a free MLOps Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Re-Run Everything?
If you only tweak the eval script, re-running ingest and train wastes time. DVC caches stage results so unchanged work is skipped. ⏱️
DVC Hashes Your Inputs
For each stage, DVC computes a hash of its dependencies and command. The hash is a fingerprint that changes the moment any input changes.
Same Hash Means Skip
When you run dvc repro, DVC compares each stage's current hash to the one in dvc.lock. If they match, it skips the stage.
See the Skip in the Output
DVC tells you plainly when nothing changed. A message like didn't change, skipping means the cached output was reused.
$ dvc repro
Stage 'prep' didn't change, skipping
Running stage 'train'...Changes Cascade Downstream
Change a prep input and DVC re-runs prep, then every downstream stage that depends on it. Upstream stages stay cached.
Outputs Are Stored in the Cache
Stage outputs are saved in DVC's cache directory, keyed by hash. Skipping a stage means DVC just restores that cached file.
Force a Full Re-Run
Need to ignore the cache? Pass --force to dvc repro and every stage runs again, even if nothing changed.
dvc repro --forceRe-Run a Single Stage
You can target one stage by name with dvc repro -s. Handy when you want to rerun just train without touching the rest.
dvc repro -s trainCheck Status Before Running
Run dvc status to see which stages are out of date without executing anything. It is a safe dry run of what repro would do.
dvc statusThe Speed Payoff
On big datasets, caching turns a thirty-minute pipeline into seconds when only one stage changed. That fast feedback loop is the whole point. ⚡
Commit dvc.lock to Share Cache State
Because dvc.lock holds the hashes, committing it lets teammates reuse the same cache decisions and skip work you already did.
Quick Check
Think about what triggers a re-run.
Recap: Cache and Skip
You saw how DVC hashes inputs to skip unchanged stages, cascade changes downstream, and force reruns when needed. Fast, smart pipelines. 🎯
Frequently asked questions
Is the “Cache and Skip Unchanged Steps” lesson free?
Yes — the full text of “Cache and Skip Unchanged Steps” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Cache and Skip Unchanged Steps”?
Re-run only what actually changed for speed. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Cache and Skip Unchanged Steps” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Stages: Ingest, Prep, Train, Eval
- Define a Pipeline with DVC Stages
- Cache and Skip Unchanged Steps
- Parameterize Runs with params.yaml