Weights & Biases (W&B)

Weights & Biases (W&B) provides MLOps infrastructure for tracking experiments, versioning datasets and models, evaluating LLM applications, and orchestrating distributed training runs.

When to Use

• Enterprise ML Experiment Tracking: Tracking hyperparameters, training loss curves, GPU utilization, and system metrics.
• Hyperparameter Tuning with W&B Sweeps: Automated Bayesian, grid, and random sweeps across distributed compute nodes.
• Model & Dataset Versioning (Artifacts): Storing, versioning, and tracking data lineage for datasets and model checkpoints.
• Production Model Evaluation & LLM Tracing: Evaluating LLM prompts and agentic tool calls with Weave / W&B Prompts.

Quick Start

import wandb

# Initialize experiment run
wandb.init(project="transformer-pretraining", config={"lr": 0.001, "batch_size": 32})

for epoch in range(10):
    # Simulated training step
    train_loss = 0.5 / (epoch + 1)
    val_acc = 0.75 + (epoch * 0.02)

    wandb.log({"epoch": epoch, "loss": train_loss, "val_accuracy": val_acc})

wandb.finish()

Core Concepts

Experiment Tracking with wandb.init & wandb.log

Logging metrics, hyperparameters, and console output:

import wandb
import numpy as np

# Initialize W&B run
run = wandb.init(
    project="transformer-pretraining",
    name="llama-3.2-finetune-v1",
    config={
        "learning_rate": 2e-5,
        "batch_size": 32,
        "epochs": 5,
        "optimizer": "AdamW",
        "weight_decay": 0.01,
        "architecture": "Llama-3.2-3B"
    }
)

# Access hyperparameter config
config = wandb.config

# Training loop simulation
for epoch in range(config.epochs):
    train_loss = 2.5 / (epoch + 1) + np.random.normal(0, 0.05)
    val_loss = 2.7 / (epoch + 1) + np.random.normal(0, 0.05)
    val_accuracy = 0.6 + 0.07 * epoch

    # Log metrics to dashboard in real-time
    wandb.log({
        "epoch": epoch,
        "train/loss": train_loss,
        "val/loss": val_loss,
        "val/accuracy": val_accuracy,
    })

wandb.finish()

Dataset & Model Checkpoint Versioning with Artifacts

Tracking data lineage and model weights:

import wandb

run = wandb.init(project="model-registry-pipeline", job_type="train")

# Log model artifact with versioning
model_artifact = wandb.Artifact(
    name="fraud_detection_model",
    type="model",
    description="Fine-tuned XGBoost model with calibrated probabilities",
    metadata={"roc_auc": 0.942, "framework": "xgboost"}
)
model_artifact.add_file("model_weights.json")
run.log_artifact(model_artifact)

# In deployment pipeline: consume specific artifact version
deployed_artifact = run.use_artifact("fraud_detection_model:latest")
artifact_dir = deployed_artifact.download()
print(f"Downloaded model to {artifact_dir}")
run.finish()

Automated Hyperparameter Sweeps with wandb.sweep

Running Bayesian optimization across parameters:

import wandb

sweep_config = {
    "method": "bayes",
    "metric": {"name": "val/loss", "goal": "minimize"},
    "parameters": {
        "learning_rate": {"min": 1e-5, "max": 1e-2},
        "batch_size": {"values": [16, 32, 64]},
        "dropout": {"uniform": [0.1, 0.5]}
    }
}

sweep_id = wandb.sweep(sweep_config, project="hyperparameter-sweeps")

def train():
    with wandb.init() as run:
        config = wandb.config
        loss = (config.learning_rate - 1e-3)**2 + config.dropout * 0.1
        wandb.log({"val/loss": loss})

# Run agent across 5 trials
# wandb.agent(sweep_id, function=train, count=5)

Common Patterns

Dataset and Model Checkpoint Versioning via Artifacts

Problem: Untracked dataset updates causing silent regressions across model training runs.

Solution: Log and consume versioned W&B Artifacts:

run = wandb.init(project="nlp-classifier")

# Log versioned training dataset
artifact = wandb.Artifact("customer_reviews", type="dataset")
artifact.add_file("data/reviews_v2.parquet")
run.log_artifact(artifact)

# In training run: link model artifact directly to input dataset lineage

Best Practices

Do:

• Always set config=... in wandb.init() to record all training hyperparameters for full experiment reproducibility.
• Call wandb.finish() at the end of runs to ensure logs, checkpoints, and system metrics finish syncing.
• Use W&B Artifacts to track datasets and models, establishing end-to-end data lineage and auditability.
• Group related runs using group="experiment_group_name" for cleaner team dashboards.

Don't:

• Call wandb.log() in tight microsecond inner loops; log aggregated metrics periodically per step or epoch.
• Hardcode your API key; configure via environment variable WANDB_API_KEY.
• Log sensitive credentials or unmasked PII data to W&B dashboards.

Troubleshooting

| Error | Cause | Solution | | :----------------------------------------------------------- | :----------------------------------------------------------------------- | :----------------------------------------------------------------------------- | | wandb.errors.CommError: It appears your API key is invalid | Missing or invalid WANDB_API_KEY. | Run wandb login or export WANDB_API_KEY. | | Network timeout during artifact upload | Uploading massive multi-gigabyte checkpoint without multipart upload. | Verify stable internet connection or save model checkpoint reference pointers. | | Multiple runs logging to the same dashboard line | Forgetting to call wandb.finish() between consecutive loop iterations. | Ensure wandb.finish() is called inside a try..finally block. |

References

• Weights & Biases