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.
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
- Maximize model performance while reducing training costs
- Enjoy simpler code, automatic checkpoints, integrations, and more
Learn more about Anyscale
Why everyone is turning to Ray
Develop on your laptop and then scale the same Python code elastically across hundreds of ndes or GPUs on any cloud — with no changes.
What You Need for the Right Scalable ML Platform
Train, test, deploy, serve, and monitor machine learning models efficiently and with speed with Ray and Anyscale.
Scale with a Click
Rely on a robust infrastructure that can scale up machine learning workflows as needed. Scale everything from XGBoost to Python to TensorFlow to Scikit-learn on top of Ray.
An Open, Broad Ecosystem
Gain to the most up-to-date technologies and their communities, don’t limit what libraries or packages you can use for your models. Load data from Snowflake, Databricks, or S3. Track your experiments with Weights & Balances or MLFlow. Or monitor your production services with Grafana. Don’t limit yourself.
Iterate quickly
Reduce friction and increase productivity by eliminating the gap between prototyping and production. Use the same tech stack regardless of environment.
What Users are Saying About Ray and Anyscale
Explore how thousands of engineers from companies of all sizes and across all verticals are tackling real-world workloads with Ray and Anyscale.
Ray has profoundly simplified the way we write scalable distributed programs for Cohere’s LLM pipelines. Its intuitive design allows us to manage complex workloads and train our models across thousands of TPUs with little to no overhead.
Siddhartha Kamalakara
Machine Learning Engineer
