Ray Tune is a scalable Python library for hyperparameter tuning and experiment management. It allows data scientists and machine learning engineers to efficiently search, optimize, and manage experiments across CPUs, GPUs, and distributed clusters.

Features of Ray Tune:

Distributed hyperparameter optimization using advanced algorithms like Bayesian Optimization, HyperBand, and Population-Based Training.
Seamless integration with popular ML frameworks such as PyTorch, TensorFlow, XGBoost, LightGBM, and Hugging Face.
Scalable execution on a single machine or large compute clusters with Ray.
Built-in experiment tracking, logging, and checkpointing for reproducible results.
Flexible search space definition for both simple and complex ML workflows.

To check the version of Ray Tune:


$ sudo su
$ sudo apt update
$ cd /opt/raytune_env
$ source venv/bin/activate
$ python -c "import ray.tune; print('Ray Tune is available and ready')"
$ python -c "import ray; print(ray.__version__)"

Disclaimer: Ray Tune is an open-source component of the Ray ecosystem, provided under the Apache 2.0 license. It is offered "as is," without any warranty, express or implied. Users are responsible for ensuring compatibility and correct integration with their ML projects.

Ray TunebCloud LLC

Ray Tune

bCloud LLC

Ray Tune

bCloud LLC

Ray Tune
bCloud LLC