Track Experiments with Weights & Biases
Log metrics, configs, and artifacts.
Track Experiments with Weights & Biases is a free Deep Learning Academy lesson on CoddyKit — lesson 1 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.
Why Track Experiments at All
You will run a model dozens of times with tiny tweaks. Without records you forget what worked. Experiment tracking logs every run so progress is never lost. 📊
Meet Weights & Biases
Weights and Biases, often written wandb, is a popular tool that records metrics, settings, and outputs from each training run in one dashboard.
pip install wandbStart a Run
One call opens a new tracked session. wandb.init names your project so every run lands in the same place for easy comparison.
import wandb
wandb.init(project="mnist-cnn")Log Your Config
Save the settings that shaped a run, like learning rate and batch size. Storing this config lets you tie any result back to its exact recipe.
wandb.config.update({"lr": 0.001, "batch_size": 32})Log Metrics Each Step
Inside your loop, send numbers you care about. wandb.log records loss and accuracy so they plot as live curves over time.
wandb.log({"loss": loss.item(), "acc": acc})Live Dashboards
Every logged number streams to a web dashboard. You watch training curves update in real time and spot a diverging run before it wastes hours.
Compare Runs Side by Side
The real power is comparison. Overlay many runs and the dashboard shows which hyperparameters actually moved your accuracy upward.
Save Artifacts
Beyond numbers you can store files. Logging a trained checkpoint as an artifact keeps the exact weights paired with the run that made them.
wandb.save("model.pt")Finish Cleanly
When training ends, close the session so all data flushes to the server. Calling wandb.finish marks the run complete and ready to review.
wandb.finish()Sweeps Search for You
Tired of tuning by hand? A wandb sweep launches many runs across a range of settings and reports which combination wins automatically.
Tracking Is a Team Habit
Logged runs become shared history. Teammates open the same dashboard and instantly see your results, making your work reproducible and easy to trust.
Quick Check
Which call records metrics like loss during training?
Recap
You learned to track runs with wandb: init a project, log config and metrics, save artifacts, and finish. Now every experiment is recorded. 🎉
Frequently asked questions
Is the “Track Experiments with Weights & Biases” lesson free?
Yes — the full text of “Track Experiments with Weights & Biases” 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 “Track Experiments with Weights & Biases”?
Log metrics, configs, and artifacts. 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 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Track Experiments with Weights & Biases” 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.
All lessons in this course
- Track Experiments with Weights & Biases
- Version Data & Models
- Detect Data & Model Drift
- Automate Retraining Pipelines