How to Build Your Own Weights & Biases

Track hyperparameters and configuration files alongside experiment runs.

Track and visualize metrics from distributed training across multiple GPUs or machines.

Create reusable experiment templates with preset configurations.

Automated hyperparameter search with Bayesian optimization and grid search capabilities.

Execute training jobs on local or cloud compute resources with parameter management.

Compare your models against industry benchmarks and baseline models.

Compare experiments across multiple projects in a single view.

Custom evaluation metrics and scoring functions for model comparison.

AI-powered analysis to identify which hyperparameters have the most impact on performance.

Powerful query language for filtering and analyzing experiment runs programmatically.

Comprehensive REST API for programmatic access to experiments and data.

Export experiment data to CSV, JSON, or connect to external analytics tools.

Run Weights & Biases locally without internet connectivity for offline development.

Send notifications to external services when experiments complete or alerts trigger.

Lightweight Python SDK for logging and tracking experiments with minimal code changes.

Track the relationship between datasets, models, and experiments for full reproducibility.

Store and manage files, models, and datasets as versioned artifacts with automatic cleanup.

Track the complete lineage of data through preprocessing and training pipelines.

Version control for datasets with lineage tracking and reproducibility.

Create public or private leaderboards to track and compare model performance.

Interactive IDE for building, testing, and versioning LLM prompts.

Track and version LLM prompts along with their inputs, outputs, and performance metrics.

AI-powered tracing and evaluation framework for tracking LLM applications end-to-end.

Create custom charts and visualizations to analyze training data and model performance.

Visualize relationships between hyperparameters and metrics with parallel coordinates.

Interactive dashboards for monitoring training progress and model metrics in real-time.

Create interactive scatter plots to analyze relationships between metrics.

Log and compare metrics, hyperparameters, and outputs from training runs automatically.

Centralized versioning and management of trained models with metadata tracking.

Complete history and lineage of all experiments with ability to restore and replay runs.

Log custom Python objects and data types alongside standard metrics.

Log images, videos, audio, and 3D objects to visualize model outputs and predictions.

Log structured data as tables for detailed analysis of predictions and errors.