Complete Weights & Biases Tutorial: ML Experiment Tracking and Visualization

Weights & Biases (W&B) is a powerful MLOps platform for experiment tracking, model visualization, and collaboration. It helps ML teams track experiments, visualize results, and share findings with automatic logging and beautiful dashboards.

Why Weights & Biases?

• Automatic logging: Track metrics, hyperparameters, code
• Beautiful visualizations: Interactive dashboards
• Collaboration: Share experiments with team
• Model registry: Version and deploy models
• Sweeps: Hyperparameter optimization

• Experiment tracking
• Hyperparameter tuning
• Model comparison
• Team collaboration
• Production monitoring

Installation

pip install wandb

Login to W&B
wandb login

Verify installation
python -c "import wandb; print(wandb.version)"

Quick Start

1. Basic Logging

import wandb

Initialize run
wandb.init(
    project="my-ml-project",
    name="experiment-1",
    config={
        "learningrate": 0.001,

        "epochs": 100,
        "batchsize": 32
    }
)

Log metrics
for epoch in range(100):
    loss = 1.0 / (epoch + 1)
    accuracy = epoch / 100

    wandb.log({
        "epoch": epoch,
        "loss": loss,
        "accuracy": accuracy
    })

Finish run
wandb.finish()

2. With PyTorch

import wandb
import torch
import torch.nn as nn

wandb.init(project="pytorch-example")

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 10)
)

Watch model
wandb.watch(model, log="all", logfreq=100)


criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    for batchidx, (data, target) in enumerate(trainloader):

        optimizer.zerograd()
        output = model(data.view(-1, 784))
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

        wandb.log({
            "batchloss": loss.item(),

            "epoch": epoch
        })

    # Log epoch metrics
    wandb.log({
        "epoch": epoch,
        "trainloss": epochloss,

        "valaccuracy": valaccuracy

    })

wandb.finish()

import wandb
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import loadiris
from sklearn.modelselection import traintestsplit

from sklearn.metrics import accuracyscore, classificationreport


wandb.init(project="sklearn-example")

Load data
X, y = loadiris(returnXy=True)
Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, testsize=0.2)


Log config
config = {
    "nestimators": 100,
    "maxdepth": 10,

    "randomstate": 42
}
wandb.config.update(config)

Train model
model = RandomForestClassifier(*config)

model.fit(Xtrain, ytrain)

Evaluate
ypred = model.predict(Xtest)
accuracy = accuracyscore(ytest, ypred)


wandb.log({
    "accuracy": accuracy,
    "classificationreport": classificationreport(ytest, ypred)

})

Log model
wandb.sklearn.plotclassifier(
    model, Xtrain, Xtest, ytrain, ytest,
    ypred, model.predictproba(Xtest),

    labels=["setosa", "versicolor", "virginica"]
)

wandb.finish()

import wandb

Method 1: Pass to init
wandb.init(
    project="my-project",
    config={
        "learningrate": 0.001,
        "architecture": "resnet50",
        "dataset": "imagenet"
    }
)

Method 2: Update config
wandb.config.update({
    "optimizer": "adam",
    "batchsize": 32