.. _tune-guides: =========== User Guides =========== .. tip:: We'd love to hear your feedback on using Tune - `get in touch `_! In this section, you can find material on how to use Tune and its various features. You can follow our :ref:`How-To Guides`, :ref:`Tune Feature Guides`, or go through some :ref:`Exercises`, to get started. .. _tune-recipes: Practical How-To Guides ----------------------- .. panels:: :container: container pb-4 full-width :column: col-md-3 px-2 py-2 :img-top-cls: pt-5 w-75 d-block mx-auto --- :img-top: /images/tune-sklearn.png +++ .. link-button:: tune-sklearn :type: ref :text: How To Use Tune's Scikit-Learn Adapters? :classes: btn-link btn-block stretched-link --- :img-top: /images/pytorch_logo.png +++ .. link-button:: tune-pytorch-cifar-ref :type: ref :text: How To Use Tune With PyTorch Models? :classes: btn-link btn-block stretched-link --- :img-top: /images/pytorch_lightning_small.png +++ .. link-button:: tune-pytorch-lightning-ref :type: ref :text: How To Tune PyTorch Lightning Models :classes: btn-link btn-block stretched-link --- :img-top: /images/serve.png +++ .. link-button:: tune-serve-integration-mnist :type: ref :text: Model Selection & Serving With Ray Serve :classes: btn-link btn-block stretched-link --- :img-top: /images/xgboost_logo.png +++ .. link-button:: tune-xgboost-ref :type: ref :text: A Guide To Tuning XGBoost Parameters With Tune :classes: btn-link btn-block stretched-link --- :img-top: /images/wandb_logo.png +++ .. link-button:: tune-wandb-ref :type: ref :text: Tracking Your Experiment Process Weights & Biases :classes: btn-link btn-block stretched-link --- :img-top: /images/mlflow.png +++ .. link-button:: tune-mlflow-ref :type: ref :text: Using MLflow Tracking & AutoLogging with Tune :classes: btn-link btn-block stretched-link --- :img-top: /images/comet_logo_full.png +++ .. link-button:: tune-comet-ref :type: ref :text: Using Comet with Ray Tune For Experiment Management :classes: btn-link btn-block stretched-link .. _tune-feature-guides: Tune Feature Guides ------------------- .. panels:: :container: container pb-4 full-width :column: col-md-3 px-2 py-2 :img-top-cls: pt-5 w-50 d-block mx-auto --- :img-top: /images/tune.png .. link-button:: tune-stopping :type: ref :text: A Guide To Stopping and Resuming Tune Experiments :classes: btn-link btn-block stretched-link --- :img-top: /images/tune.png .. link-button:: tune-metrics :type: ref :text: Using Callbacks and Metrics in Tune :classes: btn-link btn-block stretched-link --- :img-top: /images/tune.png .. link-button:: tune-output :type: ref :text: How To Log Tune Runs :classes: btn-link btn-block stretched-link --- :img-top: /images/tune.png .. link-button:: tune-resources :type: ref :text: Using Resources (GPUs, Parallel & Distributed Runs) :classes: btn-link btn-block stretched-link --- :img-top: /images/tune.png .. link-button:: tune-checkpoints :type: ref :text: Using Checkpoints For Your Experiments :classes: btn-link btn-block stretched-link --- :img-top: /images/tune.png .. link-button:: tune-lifecycle :type: ref :text: How does Tune work? :classes: btn-link btn-block stretched-link --- :img-top: /images/tune.png .. link-button:: tune-advanced-tutorial :type: ref :text: A simple guide to Population-based Training :classes: btn-link btn-block stretched-link --- :img-top: /images/tune.png .. link-button:: tune-distributed :type: ref :text: A Guide To Distributed Hyperparameter Tuning :classes: btn-link btn-block stretched-link .. _tune-exercises: Exercises --------- Learn how to use Tune in your browser with the following Colab-based exercises. .. raw:: html
Exercise Description Library Colab Link
Basics of using Tune. TF/Keras Tune Tutorial
Using Search algorithms and Trial Schedulers to optimize your model. Pytorch Tune Tutorial
Using Population-Based Training (PBT). Pytorch Tune Tutorial
Fine-tuning Huggingface Transformers with PBT. Huggingface Transformers/Pytorch Tune Tutorial
Logging Tune Runs to Comet ML. Comet Tune Tutorial
Tutorial source files `can be found here `_.