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NequIP documentation
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Contents:

  • Welcome to NequIP
  • User Guide
    • Getting Started
      • Installation
      • The NequIP Workflow
      • File Types
    • Configuration
      • Config File
      • Data Configuration
      • Model Hyperparameters
      • Loss and Metrics
    • Training Techniques
      • Fine-Tuning
      • Multitask Training
      • Weights and Biases Sweeps
    • Accelerations
      • PyTorch 2.0 Compiled Training
      • Distributed Data Parallel Training
      • GPU Kernel Modifiers
        • OpenEquivariance Acceleration
        • CuEquivariance Acceleration
      • Precision Settings
    • Reference
      • Conventions
      • FAQs
      • PyTorch Version Compatibility
      • Troubleshooting
  • Python API
    • nequip.data
      • Data Fields
      • nequip.data.datamodule
      • nequip.data.dataset
      • nequip.data.transforms
      • Data Modifiers
      • Dataset Statistics
    • nequip.train
      • NequIPLightningModule
      • Loss Function and Error Metrics
      • nequip.train.callbacks
      • nequip.train.SimpleDDPStrategy
    • nequip.nn
    • nequip.model
      • NequIP Message Passing GNN Models
      • Saved Models
    • nequip.integrations.ase
    • nequip.integrations.torchsim
  • Integrations
    • LAMMPS
      • pair_nequip_allegro
      • ML-IAP
    • ASE
    • torch-sim
    • OpenMM
  • Developer Guide
    • Contributing to NequIP
    • Understanding the NequIP Framework
      • NequIP Workflows
        • Model Packaging
    • Extension Packages
      • Getting Started
      • Data Handling
      • Training Techniques
      • Custom Models
      • Inference
    • Building Docs Locally
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AccelerationsΒΆ

Performance optimization techniques for faster training and inference.

  • PyTorch 2.0 Compiled Training
  • Distributed Data Parallel Training
  • GPU Kernel Modifiers
  • Precision Settings
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PyTorch 2.0 Compiled Training
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Weights and Biases Sweeps
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