Anyscale, the company behind Ray open source, the unified compute framework for scaling any machine learning or Python workload, announced several new advancements on the Anyscale Platform™ at AWS re:Invent in Las Vegas, NV.
The new capabilities extend beyond the advantages of Ray open source to make AI/ML and Python workload development, experimentation, and scaling even easier for developers. Today, thousands of organizations rely on Ray open source to scale AI workloads and applications, and an increasing number are turning to the Anyscale Platform™ to access a seamless Ray development and scaling experience.
For fast development and iteration, the new Anyscale Workspaces™ environment is now available for early access. Workspaces provides a unified and seamless developer experience to scale ML workloads from a laptop to the cloud with no code changes. In a single environment, developers can now build and move workloads to production while still leveraging familiar tools.
Furthermore, for accelerated development and rapid iteration, the Anyscale Platform™ adds the ability to startup clusters up to 5x faster than Ray open source, so developers can further speed up iteration, experimentation and deployments; job scheduling automation including auto-scaling, alerting, auto-retries and more; and custom cluster environments to provide organizations even more deployment and hosting flexibility.
“In the same time that it took to actually run our original workload – a week – we were able to effortlessly migrate all our Python workloads to the Anyscale Platform™, quickly fine tune jobs for scaling, and move to production at scale effortlessly,” said Jake Carter, Director Data, ML, and Technology at Biolexis Therapeutics. “It was remarkable and literally saved us a week end-to-end.”
“We are thrilled to see customers experience the benefits of the Anyscale Platform, which make Ray even more powerful and simple to use,” said Robert Nishihara, CEO and co-founder of Anyscale. “Our customers have gained tremendous value from Anyscale, and I can confidently say that we’ve just touched the tip of the iceberg on making Ray even more impactful for developers and organizations who need to accelerate AI development and experimentation and to remove the challenge of AI scaling.”
“Enabling innovations like Anyscale Platform, which are igniting a golden age in AI and machine learning, is what the AWS tech stack was built for,” said Howard Wright, VP and Global Head of Startups, AWS. “Making it easier for companies to build machine learning models that are mature, reliable, and scalable with as little as two lines of code, is the type of added value that we are excited to help bring to the market with Anyscale and Ray.”
New Capabilities and Highlights:
- Anyscale Workspaces: Workspaces provides a unified and seamless laptop like development experience to build and scale ML workloads. Workspaces enable developers to continue using the tools they are familiar with, including VS Code, Jupyter, the terminal and more, but leverage the scale and flexibility of the cloud. With a single script a developer can prepare data, tune, train and deploy workloads at any scale. As one team in a manufacturing conglomerate said, “Anyscale Workspaces allows me to go from development, to experimenting at scale, all the way to production all within the same environment. Workspace reduces context switching for us by 50%, and integrates easily with the other tools we use.”
- Fast Cluster Setup: Machine learning model training and tuning is inherently iterative, and each iteration often requires cluster startup, tuning and shutdown events. Anyscale shortens iteration cycles by taking cluster startup events down to under 2 minutes, up to 5X faster than Ray open source.
- Custom Cluster Environments: Organizations can now deploy their own custom docker images as Anyscale cluster environments and leverage their existing CI/CD pipelines to build and manage workloads running on Anyscale and Ray. This includes launching Anyscale Workspaces, jobs, and services while leveraging their own docker tooling and infrastructure.
- Job Scaling and Automation: Anyscale now provides a native way to schedule jobs in addition to integrating with best-of-breed orchestration tools like Airflow and Prefect. With Anyscale job automation and through integrations, Anyscale provides auto-scaling, alerting, auto-retries and more to simplify moving workloads to production.
Sign up for the free insideAI News newsletter.
Join us on Twitter: https://twitter.com/InsideBigData1
Join us on LinkedIn: https://www.linkedin.com/company/insidebigdata/
Join us on Facebook: https://www.facebook.com/insideAI NewsNOW
Speak Your Mind