How do I set up an experiment tracker for comparing training runs and managing model artifacts?

A good experiment tracker usually does two things:

1. Logs run metadata and metrics so you can compare training runs.
2. Stores/links model artifacts so you can version checkpoints, configs, and outputs.

Practical options

Common tools:

• MLflow: great for tracking, model registry, artifact storage, simple setup.
• Weights & Biases (W&B): excellent UI, comparisons, sweeps, collaboration.
• TensorBoard: good for metrics and visualizations, lighter-weight.
• Neptune / ClearML: full experiment management platforms.

If you want a solid default, MLflow is often the easiest place to start.

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What to track in each run

At minimum, log:

• Parameters: learning rate, batch size, optimizer, architecture, seed
• Metrics: loss, accuracy, F1, validation metrics, epoch time
• Artifacts:
  • model checkpoints
  • final trained model
  • config files
  • plots, confusion matrices, sample predictions
  • tokenizer/vocabulary/preprocessing objects
• Metadata:
  • git commit hash
  • dataset version
  • code version
  • hardware info
  • start/end time

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Recommended setup pattern

1) Create a standard run structure

For each experiment, save:

• config.yaml
• metrics.json or tracked metrics via tracker
• checkpoints/
• plots/
• logs/

2) Centralize tracking

Use a local or remote tracking backend:

• Local for solo development
• Remote server or managed service for team use

3) Name runs consistently

Example:

• resnet50_lr1e-3_bs64_seed42
• bert_base_aug_v2

4) Log everything programmatically

Make logging part of training so it’s automatic and reproducible.

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Example with MLflow

Install

pip install mlflow

Basic training logging

import mlflow
import mlflow.pytorch
import os

mlflow.set_experiment("image-classification")

with mlflow.start_run():
    mlflow.log_param("lr", 1e-3)
    mlflow.log_param("batch_size", 64)
    mlflow.log_param("epochs", 20)

    for epoch in range(20):
        train_loss = 0.42 - epoch * 0.01
        val_acc = 0.80 + epoch * 0.005

        mlflow.log_metric("train_loss", train_loss, step=epoch)
        mlflow.log_metric("val_acc", val_acc, step=epoch)

    # Save and log artifact
    os.makedirs("artifacts", exist_ok=True)
    with open("artifacts/config.yaml", "w") as f:
        f.write("lr: 0.001\nbatch_size: 64\n")

    mlflow.log_artifact("artifacts/config.yaml")

    # If using PyTorch
    # mlflow.pytorch.log_model(model, artifact_path="model")

Compare runs

MLflow UI:

mlflow ui

Then open the web interface and compare parameters/metrics across runs.

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Managing model artifacts well

Best practices:

• Save checkpoints with clear versioning:
  • checkpoint_epoch_05.pt
  • best_model.pt
• Log the best checkpoint and the final checkpoint
• Include a model_card or notes on:
  • intended use
  • training data
  • known limitations
• Store large files in object storage if needed:
  • S3, GCS, Azure Blob, MinIO

If artifacts are large, configure your tracker to point to remote storage rather than keeping everything locally.

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Make runs reproducible

Also record:

• random seed
• package versions
• CUDA/cuDNN versions
• dataset split hashes or IDs

Example:

import subprocess
git_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode().strip()
mlflow.log_param("git_commit", git_hash)

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Suggested workflow

1. Start a run.
2. Log config and environment.
3. Train model.
4. Log metrics each epoch.
5. Save best checkpoint.
6. Log artifacts.
7. Register best model if it qualifies.
8. Compare runs in the UI.
9. Promote a model to staging/production.

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If you want a lightweight alternative

You can also create your own simple tracker using:

• a SQLite/Postgres table for run metadata
• a folder or S3 bucket for artifacts
• JSON/YAML logs for metrics

But for most teams, MLflow or W&B saves a lot of time.

If you want, I can give you:

• a ready-to-use MLflow template
• a W&B setup
• or a custom tracker architecture for a small team or production system.