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Getting Started

  • Installation
  • First Steps
  • Tracking Results during Training
    • Using MLflow
    • Using Neptune.ai
    • Using Weights and Biases
    • Using File-Based Tracking
  • Saving Checkpoints during Training
  • A Toy Example with Translational Distance Models
  • Understanding the Evaluation
  • Optimizing a Model’s Hyper-parameters
  • Novel Link Prediction
  • Running an Ablation Study
  • Performance Tricks

Bring Your Own

  • Bring Your Own Data
  • Bring Your Own Interaction

Extending PyKEEN

  • Extending the Datasets
  • Extending the Models
  • Extending the Interaction Models (Old-Style)

Reference

  • Pipeline
  • Models
  • Datasets
  • Triples
  • Training
  • Stoppers
  • Loss Functions
  • Regularizers
  • Result Trackers
  • Evaluation
  • Negative Sampling
  • Hyper-parameter Optimization
  • Ablation
  • Lookup
  • Prediction
  • Sealant
  • Constants
  • pykeen.nn
  • Utilities

Appendix

  • References
pykeen
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  • Tracking Results during Training
  • Edit on GitHub

Tracking Results during Training¶

  • Using MLflow
    • Pipeline Example
    • HPO Example
    • Reusing Experiments
    • Adding Tags
  • Using Neptune.ai
    • Preparation
    • Pipeline Example
    • Reusing Experiments
    • Adding Tags
  • Using Weights and Biases
    • Pipeline Example
    • HPO Example
  • Using File-Based Tracking
    • Minimal Pipeline Example with CSV
    • Specifying a Name
    • Combining with tail
    • Pipeline Example with JSON
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