Introduction to Comet ML

Comet ML is a machine learning experiment tracking platform that logs metrics, hyperparameters, code state, and artifacts for every training run, letting teams compare experiments systematically instead of relying on scattered notebooks and spreadsheets.

Core Concepts

Experiments

An experiment is a single tracked training run. Comet automatically captures the metrics you log, along with the git commit, installed dependencies, and system information, so a past run can be reproduced or audited later.

Projects

Experiments are grouped into projects, which correspond to a specific model or task being developed. Within a project, Comet provides a comparison view across all logged experiments.

Panels

Panels are customizable visualizations (line charts, parallel coordinate plots, confusion matrices) that can be arranged into dashboards for monitoring training progress or comparing runs side by side.

Getting Started

Install and Configure

pip install comet_ml
export COMET_API_KEY="your-api-key"

Logging an Experiment

from comet_ml import Experiment

experiment = Experiment(project_name="image-classifier")

experiment.log_parameter("learning_rate", 0.001)
experiment.log_parameter("batch_size", 64)

for epoch in range(epochs):
    train_loss = train_one_epoch(model, data)
    experiment.log_metric("train_loss", train_loss, step=epoch)

experiment.log_model("final-model", "model.pt")
experiment.end()

Comparing Experiments

Comet’s comparison view overlays metrics from multiple experiments on the same chart, making it straightforward to see which hyperparameter combination produced the best validation performance:

experiment A: lr=0.001, batch=64  -> val_acc=0.91
experiment B: lr=0.0005, batch=32 -> val_acc=0.93
experiment C: lr=0.001, batch=128 -> val_acc=0.89

Parallel coordinate plots visualize how multiple hyperparameters jointly relate to the target metric across many runs at once, which is harder to see from a flat table.

Model Registry

Comet includes a model registry that versions trained models, tracks which experiment produced each version, and records the stage a model is in (staging, production, archived). This gives a clear audit trail connecting a deployed model back to the exact training run, code, and data that produced it.

Model Monitoring

Beyond training, Comet can track live production model performance — logging prediction distributions and drift metrics over time so degradation can be caught before it significantly affects downstream decisions.

Comet ML vs. Other Tracking Tools

Common Use Cases

• Hyperparameter comparison: tracking many runs to find the best-performing configuration.
• Reproducibility: recreating a past experiment’s exact code, data, and environment.
• Team collaboration: sharing experiment dashboards across a team without manually exporting charts.
• Production monitoring: tracking a deployed model’s live performance against its training-time baseline.

Comet ML’s combination of experiment tracking, model registry, and production monitoring makes it a full-lifecycle tool for teams that need traceability from initial training run through deployed model.