pykeen
Getting Started
Installation
First Steps
Knowledge Graph Embedding Models
Tracking Results during Training
Using MLflow
Using Neptune.ai
Using Weights and Biases
Using Tensorboard
Using File-Based Tracking
Saving Checkpoints during Training
A Toy Example with Translational Distance Models
Understanding the Evaluation
Optimizing a Model’s Hyper-parameters
Running an Ablation Study
Performance Tricks
Representations
Getting Started with NodePiece
Inductive Link Prediction
Splitting
PyTorch Lightning Integration
Using Resolvers
Troubleshooting
Bring Your Own
Bring Your Own Data
Bring Your Own Interaction
Extending PyKEEN
Extending the Datasets
Extending the Models
Reference
Pipeline
Models
Datasets
Inductive Datasets
Entity Alignment
Triples
Training
Stoppers
Loss Functions
Regularizers
Result Trackers
Negative Sampling
Filtering
Evaluation
Metrics
Hyper-parameter Optimization
Ablation
Lookup
Prediction
Uncertainty
Sealant
Constants
Flexible Weight Checkpoints
pykeen.nn
Utilities
Appendix
Analysis
References
pykeen
Tracking Results during Training
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Tracking Results during Training
Result Trackers
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 Tensorboard
Installing Tensorboard
Starting Tensorboard
Minimal Pipeline Example
Specifying a Log Name
Specifying a Custom Log Directory
Minimal HPO Pipeline Example
Using File-Based Tracking
Minimal Pipeline Example with CSV
Specifying a Name
Combining with
tail
Pipeline Example with JSON