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WeightsLab
WeightsLab WeightsLab
WeightsLab

GETTING STARTED

  • Quickstart
  • Agent Quickstart
  • Good Practice
    • Heavy-experiment loader flags
    • Implementing get_items in your dataset class
    • Training loop
    • Signals and storage
    • Summary table

WEIGHTS STUDIO

  • Weights Studio UI
    • Landing page
    • Agent
    • Left panel
    • Main area
    • More
      • Ports and remote access
      • Secure mode (HTTPS + mTLS)
      • Configuration reference
      • Deployment and building
      • Troubleshooting
  • Weights Studio CLI
    • Starting and connecting
    • Console commands

EXAMPLES

  • Examples
    • PyTorch Basics
      • Classification — MNIST (PyTorch)
      • Segmentation — BDD100k (PyTorch)
      • Detection — Penn-Fudan Pedestrians (PyTorch)
      • Clustering — Face Recognition (PyTorch)
      • Generation / Anomaly Detection — MVTec (PyTorch)
    • Lightning
      • Classification — MNIST (PyTorch Lightning)
    • Ultralytics
      • Detection — YOLO (Ultralytics)
    • Specific User Usecases
      • LiDAR Detection — 2D and 3D (PyTorch)
      • Loss-Shape Classification per Sample
      • Model Signals on Fashion-MNIST

CORE CONCEPTS

  • Four-Way SDK Approach
    • Model Interaction
    • Data Exploration
    • Config Management
    • Logger and Signals
  • Signal Trajectory Classification
  • Custom Evaluation Function

TOOLS

  • Resource Monitoring
  • Experiment Reports
  • Experiment Versioning
  • Experiment Agent Assistant
  • Annotation Export

INTEGRATIONS

  • Notebook integrations
  • PyTorch Lightning Integration
  • Ultralytics Integration

CONFIGURATION

  • Configuration

REFERENCE

  • User Functions Reference
  • User Commands Reference
  • gRPC
    • gRPC Functions
    • Audit Logger
  • What’s New
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