Convert existing PyTorch code to Ray AIR: Get started with Ray AIR from an existing PyTorch codebase
Convert existing Tensorflow/Keras code to Ray AIR: Get started with Ray AIR from an existing Tensorflow/Keras codebase.
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)
Simple Machine Learning#
Simple AutoML for time series with Ray AIR: Build an AutoML system for time-series forecasting with Ray AIR
Batch training & tuning on Ray Tune: Perform batch tuning on NYC Taxi Dataset with Ray AIR
Batch (parallel) Demand Forecasting using Prophet, ARIMA, and Ray Tune: Perform batch forecasting on NYC Taxi Dataset with Prophet, ARIMA and Ray AIR
Fine-tune a 🤗 Transformers model: How to use Ray AIR to run Hugging Face Transformers fine-tuning on a text classification task.
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.