Weights & Biases by CoreWeave
Tracks and visualizes machine learning experiments, streamlining model development
Weights & Biases (W&B) is a platform for tracking, visualizing, and managing machine learning experiments and LLM applications. It integrates with frameworks like PyTorch, TensorFlow, and Hugging Face to log metrics, hyperparameters, and artifacts. The platform consists of three components: W&B Core for experiment tracking, W&B Models for training and fine-tuning, and W&B Weave for LLM evaluation and monitoring. Users can access a web-based dashboard to visualize metrics like loss and accuracy, compare runs, and automate hyperparameter tuning with Sweeps.
The platform supports collaboration by centralizing experiment data, making it accessible to teams. Artifacts manage dataset and model versioning, ensuring reproducibility. W&B offers a free tier for academics and personal projects, with paid plans for corporate use that include unlimited tracking hours and 200GB of cloud storage. The Weave toolkit logs LLM inputs, outputs, and token usage, providing insights into performance and cost.
Compared to Comet, which focuses on simplicity, or Neptune, which emphasizes dataset management, W&B offers broader framework support and LLM-specific tools. However, some users report challenges with remote team collaboration, often requiring external tools. The interface may overwhelm beginners due to its extensive features.
W&B’s integrations cover popular libraries like LangChain and XGBoost, making it versatile for various AI workflows. The platform hosts the W&B AI Academy, offering free courses on MLOps and LLMOps, and organizes events like Fully Connected for community learning. The free tier is robust for individual researchers, but corporate users should evaluate storage and tracking limits based on their needs.
To get started, install the W&B SDK with pip, add a few lines to your script, and log metrics to the dashboard. Explore the Weave documentation for LLM tracking or try the Sweeps feature for hyperparameter optimization. Check the W&B Community on Discord for support and insights from other users.
Homepage Screenshot 📸
Video Overview 🎬
What are the key features? ✨
- Experiment Tracking: Logs metrics, hyperparameters, and code for ML experiments.
- Weave Toolkit: Tracks and evaluates LLM inputs, outputs, and token usage.
- Sweeps: Automates hyperparameter tuning for optimized model performance.
- Artifacts: Manages dataset and model versioning for reproducibility.
- Dashboard: Visualizes experiment data with customizable charts and graphs.
Who is it for? 🤔
Examples of what you can use it for 💡
- Data Scientist: Uses W&B to log and compare model performance metrics across multiple runs.
- ML Engineer: Employs Sweeps to optimize hyperparameters for a deep learning model.
- LLM Developer: Tracks LLM inputs and outputs with Weave for debugging and evaluation.
- Academic Researcher: Leverages the free tier to manage experiments and share results.
- Team Lead: Centralizes experiment data for team collaboration and reproducibility.
Pros & Cons ⚖️
- Integrates with many ML frameworks.
- Free for academic and personal use.
- Automates hyperparameter tuning.
- Collaboration can feel clunky.
- Custom metrics setup is complex.
FAQs 💬
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Tracks and visualizes machine learning experiments, streamlining model development
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