External library integrations for Ray Tune
Contents
External library integrations for Ray Tune#
Tune Experiment Monitoring Integrations#
Comet (air.integrations.comet)#
|
CometLoggerCallback for logging Tune results to Comet. |
MLflow (air.integrations.mlflow)#
|
MLflow Logger to automatically log Tune results and config to MLflow. |
|
Set up a MLflow session. |
Weights and Biases (air.integrations.wandb)#
|
Weights and biases (https://www.wandb.ai/) is a tool for experiment tracking, model optimization, and dataset versioning. |
|
Set up a Weights & Biases session. |
Integrations with ML Libraries#
Keras (air.integrations.keras)#
|
Keras callback for Ray AIR reporting and checkpointing. |
MXNet (tune.integration.mxnet)#
|
MXNet to Ray Tune reporting callback |
|
MXNet checkpoint callback |
PyTorch Lightning (tune.integration.pytorch_lightning)#
|
PyTorch Lightning to Ray Tune reporting callback |
|
PyTorch Lightning report and checkpoint callback |
XGBoost (tune.integration.xgboost)#
|
XGBoost to Ray Tune reporting callback |
|
XGBoost report and checkpoint callback |
LightGBM (tune.integration.lightgbm)#
|
Create a callback that reports metrics to Ray Tune. |
|
Creates a callback that reports metrics and checkpoints model. |