Hyperparameter Tuning

Hyperparameter Tuning Distributed and at Scale

Hyperparameter tuning is key to controlling the behavior of machine learning models. If not done correctly, estimated model parameters produce suboptimal results with more errors. Building model parameters without tuning hyperparameters may work but will always be less accurate than a model that has tuned hyperparameters. Additionally, most methods are can be tedious and time consuming.

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With Ray Tune and Anyscale, you can do it all and at scale. You can accelerate the search for the right hyperparameters by distributing the work in parallel across various machines. Additionally, Ray Tune lets you:

Be library agnostic and work with the most popular ML frameworks

Enjoy simpler code, automatic checkpoints, integrations, and more

Maximize model performance while reducing training costs

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Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud — with no changes.

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Hyperparameter Tuning

State of the art algorithms

Leverage a variety of cutting edge optimization algorithms, reducing the cost of tuning by terminating bad runs early, choosing better parameters to evaluate, or even changing the hyperparameters during training to optimize schedules.

Fast and Easy Distributed Hyperparameter Tuning

Distributed training out of the box

Avoid having to implement your own multi-process framework or build your own distributed system to speed up hyperparameter tuning. Instead, parallelize across multiple GPUs and nods. Alos, scale up hyperparameter searches by 100x while reducing cost by up to 10x with preemtible instances.

Increased developer productivity

Why restructure code when you don’t have to. Optimize models with just a few code snippets. Remove boilerplate from your code training workflow, automatically manage checkpoints and logs in like MLFlow and TensorBoard.

Power up existing workflows with minimal code changes

Ray Tune’s Search Algorithms integrate with a variety of popular hyperparameter tuning libraries and tools such as HyperOpt or Bayesian Optimization. Seamlessly scale up your optimization process - without sacrificing performance.

Emiliano Castro, Principal Data Scientist, WildLife

"Ray and Anyscale have enabled us to quickly develop, test and deploy a new in-game offer recommendation engine based on reinforcement learning, and subsequently serve those offers 3X faster in production. This resulted in revenue lift and a better gaming experience."

Greg Brockman, Co-founder, Chairman, & President, OpenAI

"At OpenAI, we are tackling some of the world’s most complex and demanding computational problems. Ray powers our solutions to the thorniest of these problems and allows us to iterate at scale much faster than we could before. As an example, we use Ray to train our largest models, including ChatGPT."

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