mlops-weights-and-biases

原文涵盖的主题

• Weights & Biases: ML Experiment Tracking & MLOps
• When to Use This Skill
• Installation
• Quick Start
• Basic Experiment Tracking
• With PyTorch
• Core Concepts
• 1. Projects and Runs

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+ Project: Collection of related experiments Run: Single execution of your training script Track hyperparameters automatically: Automatically search for optimal hyperparameters. Track datasets, models, and other files with lineage. Create shareable reports in W&B UI: Combine runs, charts, and text Markdown support Embeddable visualizations Team collaboration Create team account at wandb.ai Add team members Set project visibility (private/public) Use team-level artifacts and model registry 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 Documentation: https://docs.wandb.ai GitHub: htt

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