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Weights And Biases

orchestra-research/ai-research-skills

  • 436 installs
  • 11.2k repo stars
  • Updated June 16, 2026
  • orchestra-research/ai-research-skills

weights-and-biases is an agent skill that documents W&B Artifacts and Model Registry workflows for versioned datasets and model checkpoints.

About

weights-and-biases is a research-oriented agent skill that teaches solo and indie ML builders how to use Weights & Biases Artifacts and the Model Registry as the system of record for datasets, checkpoints, preprocessing outputs, and evaluation bundles. Instead of losing track of which CSV or torch.save file belonged to which experiment, you log versioned artifacts with descriptions and metadata, attach files or cloud references, and rely on W&B’s deduplicated storage and lineage graph to see which runs consumed or produced each artifact. The guide walks through wandb.init, constructing dataset and model artifacts, logging them from training loops, and applying aliases for production promotion workflows common in small teams shipping fine-tuned models or custom eval harnesses. Prism places it on Build integrations because the first value is wiring the SDK into your codebase, but the same patterns support Ship reproducibility checks and Operate model rollbacks. Agents load it when you ask for artifact versioning, registry aliases, or reproducible ML delivery without rebuilding tribal knowledge from scattered notebooks.

  • End-to-end Artifacts guide: creation, usage, Model Registry, versioning, and lineage
  • Dataset and model artifact patterns with metadata blocks and add_file, add_dir, add_reference
  • Automatic versioning (v0, v1, v2) plus aliases such as latest, best, and production
  • Lineage tracking tying runs to datasets and checkpoints for reproducibility
  • Best-practices section for team-wide artifact collaboration and deduplicated storage

Weights And Biases by the numbers

  • 436 all-time installs (skills.sh)
  • +32 installs in the week ending Jul 26, 2026 (Skillselion tracking)
  • Ranked #466 of 2,066 Data Science & ML skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
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Last updatedJune 16, 2026
Repositoryorchestra-research/ai-research-skills ↗

What it does

Version datasets and model checkpoints in Weights & Biases Artifacts and Model Registry so ML developers can reproduce runs and promote models without scattered files.

Who is it for?

Best when you're fine-tuning models or running repeatable evals and already use or plan to use W&B for experiment tracking.

Skip if: Projects with no ML training pipeline, or teams that need a non-W&B MLOps platform without SDK migration appetite.

When should I use this skill?

You need W&B Artifacts, Model Registry, dataset or checkpoint versioning, or lineage tracking in training code.

What you get

Your agent implements wandb Artifact logging with metadata, lineage, and registry aliases so runs and promoted models stay reproducible across environments.

  • Artifact logging code for datasets and/or models with metadata
  • Registry-oriented promotion pattern using aliases such as latest or production

By the numbers

  • Table of contents covers 6 major sections including Model Registry and Best Practices
  • Documents automatic artifact versioning pattern v0, v1, v2

Files

SKILL.mdMarkdownGitHub ↗

Weights & Biases: ML Experiment Tracking & MLOps

When to Use This Skill

Use Weights & Biases (W&B) when you need to:

  • Track ML experiments with automatic metric logging
  • Visualize training in real-time dashboards
  • Compare runs across hyperparameters and configurations
  • Optimize hyperparameters with automated sweeps
  • Manage model registry with versioning and lineage
  • Collaborate on ML projects with team workspaces
  • Track artifacts (datasets, models, code) with lineage

Users: 200,000+ ML practitioners | GitHub Stars: 10.5k+ | Integrations: 100+

Installation

# Install W&B
pip install wandb

# Login (creates API key)
wandb login

# Or set API key programmatically
export WANDB_API_KEY=your_api_key_here

Quick Start

Basic Experiment Tracking

import wandb

# Initialize a run
run = wandb.init(
    project="my-project",
    config={
        "learning_rate": 0.001,
        "epochs": 10,
        "batch_size": 32,
        "architecture": "ResNet50"
    }
)

# Training loop
for epoch in range(run.config.epochs):
    # Your training code
    train_loss = train_epoch()
    val_loss = validate()

    # Log metrics
    wandb.log({
        "epoch": epoch,
        "train/loss": train_loss,
        "val/loss": val_loss,
        "train/accuracy": train_acc,
        "val/accuracy": val_acc
    })

# Finish the run
wandb.finish()

With PyTorch

import torch
import wandb

# Initialize
wandb.init(project="pytorch-demo", config={
    "lr": 0.001,
    "epochs": 10
})

# Access config
config = wandb.config

# Training loop
for epoch in range(config.epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        # Forward pass
        output = model(data)
        loss = criterion(output, target)

        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Log every 100 batches
        if batch_idx % 100 == 0:
            wandb.log({
                "loss": loss.item(),
                "epoch": epoch,
                "batch": batch_idx
            })

# Save model
torch.save(model.state_dict(), "model.pth")
wandb.save("model.pth")  # Upload to W&B

wandb.finish()

Core Concepts

1. Projects and Runs

Project: Collection of related experiments Run: Single execution of your training script

# Create/use project
run = wandb.init(
    project="image-classification",
    name="resnet50-experiment-1",  # Optional run name
    tags=["baseline", "resnet"],    # Organize with tags
    notes="First baseline run"      # Add notes
)

# Each run has unique ID
print(f"Run ID: {run.id}")
print(f"Run URL: {run.url}")

2. Configuration Tracking

Track hyperparameters automatically:

config = {
    # Model architecture
    "model": "ResNet50",
    "pretrained": True,

    # Training params
    "learning_rate": 0.001,
    "batch_size": 32,
    "epochs": 50,
    "optimizer": "Adam",

    # Data params
    "dataset": "ImageNet",
    "augmentation": "standard"
}

wandb.init(project="my-project", config=config)

# Access config during training
lr = wandb.config.learning_rate
batch_size = wandb.config.batch_size

3. Metric Logging

# Log scalars
wandb.log({"loss": 0.5, "accuracy": 0.92})

# Log multiple metrics
wandb.log({
    "train/loss": train_loss,
    "train/accuracy": train_acc,
    "val/loss": val_loss,
    "val/accuracy": val_acc,
    "learning_rate": current_lr,
    "epoch": epoch
})

# Log with custom x-axis
wandb.log({"loss": loss}, step=global_step)

# Log media (images, audio, video)
wandb.log({"examples": [wandb.Image(img) for img in images]})

# Log histograms
wandb.log({"gradients": wandb.Histogram(gradients)})

# Log tables
table = wandb.Table(columns=["id", "prediction", "ground_truth"])
wandb.log({"predictions": table})

4. Model Checkpointing

import torch
import wandb

# Save model checkpoint
checkpoint = {
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'loss': loss,
}

torch.save(checkpoint, 'checkpoint.pth')

# Upload to W&B
wandb.save('checkpoint.pth')

# Or use Artifacts (recommended)
artifact = wandb.Artifact('model', type='model')
artifact.add_file('checkpoint.pth')
wandb.log_artifact(artifact)

Hyperparameter Sweeps

Automatically search for optimal hyperparameters.

Define Sweep Configuration

sweep_config = {
    'method': 'bayes',  # or 'grid', 'random'
    'metric': {
        'name': 'val/accuracy',
        'goal': 'maximize'
    },
    'parameters': {
        'learning_rate': {
            'distribution': 'log_uniform',
            'min': 1e-5,
            'max': 1e-1
        },
        'batch_size': {
            'values': [16, 32, 64, 128]
        },
        'optimizer': {
            'values': ['adam', 'sgd', 'rmsprop']
        },
        'dropout': {
            'distribution': 'uniform',
            'min': 0.1,
            'max': 0.5
        }
    }
}

# Initialize sweep
sweep_id = wandb.sweep(sweep_config, project="my-project")

Define Training Function

def train():
    # Initialize run
    run = wandb.init()

    # Access sweep parameters
    lr = wandb.config.learning_rate
    batch_size = wandb.config.batch_size
    optimizer_name = wandb.config.optimizer

    # Build model with sweep config
    model = build_model(wandb.config)
    optimizer = get_optimizer(optimizer_name, lr)

    # Training loop
    for epoch in range(NUM_EPOCHS):
        train_loss = train_epoch(model, optimizer, batch_size)
        val_acc = validate(model)

        # Log metrics
        wandb.log({
            "train/loss": train_loss,
            "val/accuracy": val_acc
        })

# Run sweep
wandb.agent(sweep_id, function=train, count=50)  # Run 50 trials

Sweep Strategies

# Grid search - exhaustive
sweep_config = {
    'method': 'grid',
    'parameters': {
        'lr': {'values': [0.001, 0.01, 0.1]},
        'batch_size': {'values': [16, 32, 64]}
    }
}

# Random search
sweep_config = {
    'method': 'random',
    'parameters': {
        'lr': {'distribution': 'uniform', 'min': 0.0001, 'max': 0.1},
        'dropout': {'distribution': 'uniform', 'min': 0.1, 'max': 0.5}
    }
}

# Bayesian optimization (recommended)
sweep_config = {
    'method': 'bayes',
    'metric': {'name': 'val/loss', 'goal': 'minimize'},
    'parameters': {
        'lr': {'distribution': 'log_uniform', 'min': 1e-5, 'max': 1e-1}
    }
}

Artifacts

Track datasets, models, and other files with lineage.

Log Artifacts

# Create artifact
artifact = wandb.Artifact(
    name='training-dataset',
    type='dataset',
    description='ImageNet training split',
    metadata={'size': '1.2M images', 'split': 'train'}
)

# Add files
artifact.add_file('data/train.csv')
artifact.add_dir('data/images/')

# Log artifact
wandb.log_artifact(artifact)

Use Artifacts

# Download and use artifact
run = wandb.init(project="my-project")

# Download artifact
artifact = run.use_artifact('training-dataset:latest')
artifact_dir = artifact.download()

# Use the data
data = load_data(f"{artifact_dir}/train.csv")

Model Registry

# Log model as artifact
model_artifact = wandb.Artifact(
    name='resnet50-model',
    type='model',
    metadata={'architecture': 'ResNet50', 'accuracy': 0.95}
)

model_artifact.add_file('model.pth')
wandb.log_artifact(model_artifact, aliases=['best', 'production'])

# Link to model registry
run.link_artifact(model_artifact, 'model-registry/production-models')

Integration Examples

HuggingFace Transformers

from transformers import Trainer, TrainingArguments
import wandb

# Initialize W&B
wandb.init(project="hf-transformers")

# Training arguments with W&B
training_args = TrainingArguments(
    output_dir="./results",
    report_to="wandb",  # Enable W&B logging
    run_name="bert-finetuning",
    logging_steps=100,
    save_steps=500
)

# Trainer automatically logs to W&B
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

trainer.train()

PyTorch Lightning

from pytorch_lightning import Trainer
from pytorch_lightning.loggers import WandbLogger
import wandb

# Create W&B logger
wandb_logger = WandbLogger(
    project="lightning-demo",
    log_model=True  # Log model checkpoints
)

# Use with Trainer
trainer = Trainer(
    logger=wandb_logger,
    max_epochs=10
)

trainer.fit(model, datamodule=dm)

Keras/TensorFlow

import wandb
from wandb.keras import WandbCallback

# Initialize
wandb.init(project="keras-demo")

# Add callback
model.fit(
    x_train, y_train,
    validation_data=(x_val, y_val),
    epochs=10,
    callbacks=[WandbCallback()]  # Auto-logs metrics
)

Visualization & Analysis

Custom Charts

# Log custom visualizations
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(x, y)
wandb.log({"custom_plot": wandb.Image(fig)})

# Log confusion matrix
wandb.log({"conf_mat": wandb.plot.confusion_matrix(
    probs=None,
    y_true=ground_truth,
    preds=predictions,
    class_names=class_names
)})

Reports

Create shareable reports in W&B UI:

  • Combine runs, charts, and text
  • Markdown support
  • Embeddable visualizations
  • Team collaboration

Best Practices

1. Organize with Tags and Groups

wandb.init(
    project="my-project",
    tags=["baseline", "resnet50", "imagenet"],
    group="resnet-experiments",  # Group related runs
    job_type="train"             # Type of job
)

2. Log Everything Relevant

# Log system metrics
wandb.log({
    "gpu/util": gpu_utilization,
    "gpu/memory": gpu_memory_used,
    "cpu/util": cpu_utilization
})

# Log code version
wandb.log({"git_commit": git_commit_hash})

# Log data splits
wandb.log({
    "data/train_size": len(train_dataset),
    "data/val_size": len(val_dataset)
})

3. Use Descriptive Names

# ✅ Good: Descriptive run names
wandb.init(
    project="nlp-classification",
    name="bert-base-lr0.001-bs32-epoch10"
)

# ❌ Bad: Generic names
wandb.init(project="nlp", name="run1")

4. Save Important Artifacts

# Save final model
artifact = wandb.Artifact('final-model', type='model')
artifact.add_file('model.pth')
wandb.log_artifact(artifact)

# Save predictions for analysis
predictions_table = wandb.Table(
    columns=["id", "input", "prediction", "ground_truth"],
    data=predictions_data
)
wandb.log({"predictions": predictions_table})

5. Use Offline Mode for Unstable Connections

import os

# Enable offline mode
os.environ["WANDB_MODE"] = "offline"

wandb.init(project="my-project")
# ... your code ...

# Sync later
# wandb sync <run_directory>

Team Collaboration

Share Runs

# Runs are automatically shareable via URL
run = wandb.init(project="team-project")
print(f"Share this URL: {run.url}")

Team Projects

  • Create team account at wandb.ai
  • Add team members
  • Set project visibility (private/public)
  • Use team-level artifacts and model registry

Pricing

  • Free: Unlimited public projects, 100GB storage
  • Academic: Free for students/researchers
  • Teams: $50/seat/month, private projects, unlimited storage
  • Enterprise: Custom pricing, on-prem options

Resources

  • Documentation: https://docs.wandb.ai
  • GitHub: https://github.com/wandb/wandb (10.5k+ stars)
  • Examples: https://github.com/wandb/examples
  • Community: https://wandb.ai/community
  • Discord: https://wandb.me/discord

See Also

  • references/sweeps.md - Comprehensive hyperparameter optimization guide
  • references/artifacts.md - Data and model versioning patterns
  • references/integrations.md - Framework-specific examples

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How it compares

W&B Artifacts and registry integration guide—not a NeMo infra resiliency skill and not a generic pytest workflow.

FAQ

Who is weights-and-biases for?

ML and LLM developers who want agent help implementing W&B artifact versioning, lineage, and model promotion patterns in Python training code.

When should I use weights-and-biases?

Use it during Build when integrating wandb into training; during Ship when packaging checkpoints for release; and during Grow when comparing artifact-tagged eval results across versions.

Is weights-and-biases safe to install?

The skill teaches cloud-backed logging and file uploads—store API keys outside the repo and review the Security Audits panel on this Prism page before granting network or secrets access to the agent.

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