Hyperparameter Tuning distributed and at scale
Accelerate hyperparameter tuning
Challenge
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.
Solutions
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:
- Maximize model performance while reducing training costs
- Be library agnostic and work with the most popular ML frameworks
- Enjoy simpler code, automatic checkpoints, integrations, and more
Fast and easy distributed 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.
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.
Iterate and move to production fast with Ray Serve and Anyscale
LinkedIn improved member engagement with a superior Network Quality Service prediction model. See their path to 2x faster training with Ray Tune.
Learn how Anastasia realized 9x speedup and 87% cost reduction on their demand forecasting use case with Ray Tune.
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