Examples¶
Framework-specific Examples¶
Training a model with distributed LightGBM: Distributed training with LightGBM
Training a model with distributed XGBoost: Distributed training with XGBoost
Hyperparameter tuning with XGBoostTrainer: Distributed tuning with XGBoost
Training a model with Sklearn: Integrating with Scikit-Learn (non-distributed)
Convert existing PyTorch code to Ray AIR: How to get started with Ray AIR from your code base
Tabular Data¶
Tabular data training and serving with Keras and Ray AIR: How to use Ray AIR to train a Keras model on tabular data and serve it.
Text/NLP¶
Fine-tune a 🤗 Transformers model: How to use Ray AIR to run Hugging Face Transformers fine-tuning on a text classification task.
Image/CV¶
Logging & Observability¶
Logging results and uploading models to Comet ML: How to log results and upload models to Comet ML.
Logging results and uploading models to Weights & Biases: How to log results and upload models to Weights and Biases.
RL (RLlib)¶
Advanced¶
Incremental Learning with Ray AIR: Incrementally train and deploy a PyTorch CV model
Integrate Ray AIR with Feast feature store: Integrate with Feast feature store in both train and inference