Version Data & Models
Reproduce any run from its inputs.
Version Data & Models is a free Deep Learning Academy lesson on CoddyKit — lesson 2 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 Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Code Versioning Is Not Enough
Git tracks your code beautifully, but a model also depends on data and weights. To reproduce a result you must version those too. 🗂️
Why Data Changes Break Runs
Datasets grow, get cleaned, or get relabeled. If you cannot say which version of the data trained a model, you can never rebuild that exact result.
The Reproducibility Triangle
A run is reproducible only when three things are pinned together: the code, the data, and the trained weights. Drop any one and the result drifts.
Hash the Data
You do not store huge files in Git. Instead you record a hash, a short fingerprint of the dataset, so any change is instantly detected.
import hashlib
h = hashlib.md5(open("train.csv","rb").read()).hexdigest()Meet DVC
DVC, Data Version Control, layers on top of Git to version large files. It stores a tiny pointer in Git and the real data in remote storage.
pip install dvcTrack a Dataset
One command tells DVC to manage a file. It replaces the heavy data with a small .dvc pointer that Git can safely commit.
dvc add data/train.csvPush Data to Remote
The actual bytes live in cloud storage, not your repo. dvc push uploads them so teammates can pull the exact same files later.
dvc pushVersion the Model Too
Save weights with a clear name that ties them to a run. Pairing a checkpoint with its commit and data hash makes the model fully traceable.
torch.save(model.state_dict(), "model_v3.pt")Tag Releases
When a model is good enough to ship, mark that moment. A Git tag like v1.0 lets you return to the exact code, data, and weights anytime.
git tag -a v1.0 -m "first production model"Reproduce Any Run
With everything versioned, recovery is two steps: checkout the commit, then dvc pull. You get the identical inputs that produced the original model.
git checkout v1.0
dvc pullVersioning Builds Trust
When anyone can rebuild a result from scratch, your work becomes auditable. That trust is what separates a hobby project from production ML.
Quick Check
How does DVC keep large datasets out of Git?
Recap
You learned to version data and models: hash inputs, track files with DVC, push to remote, and tag releases so any run is reproducible. 🎉
Frequently asked questions
Is the “Version Data & Models” lesson free?
Yes — the full text of “Version Data & Models” is free to read here on the web, and the Deep Learning 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 Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Version Data & Models”?
Reproduce any run from its inputs. You practise Deep Learning 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 Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Version Data & Models” 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 Deep Learning Academy lesson?
Yes. Every Deep Learning 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.