管理数据与模型版本
根据输入复现任意一次运行
管理数据与模型版本 是 CoddyKit 上的免费 Deep Learning Academy 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
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. 🎉
常见问题解答
「管理数据与模型版本」课时是免费的吗？
是的 — 「管理数据与模型版本」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。
「管理数据与模型版本」这节课中我会学到什么？
根据输入复现任意一次运行 你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Deep Learning Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「管理数据与模型版本」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Deep Learning Academy 课中编写并运行代码吗？
能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。