Welcome to this Ryax documentation!
This is the best place to start with Ryax. We cover what Ryax is, what problems it can solve, and how it compares to existing software. If you want to get started as fast as possible, writing modules and workflows jump directly to the main tutorial.
What is Ryax?¶
Ryax is the answer to your data science industrialization problems. No more ad-hoc solutions that only a single person understands. Be done with the hassle of deploying and running periodically. Easily reuse pieces of code amongst different projects. Leverage the most powerful tools for data scientists: pytorch, TensorFlow, matplotlib, and more. Ryax is a platform for Data Teams (Data Scientists, Engineers…), which helps them transform, automate, and deploy their data projects into business opportunities.
Some of Ryax’s key benefits:
Automation: auto deployment, multi-node orchestration, native error management and resume, data consistency management… Ryax robustly automates your Data Science execution.
Observability: monitor any point, report effortlessly internally and externally with Ryax’s integrated features (execution logs, multi-node monitoring and full-traceability)
Evolutivity: naturally grow towards new analytics (ML, AI…), new infrastructures (Hybrid Cloud, Edge-to-Cloud) and best practices (CI/CD processes, code modularity…)
Low code: drop your models, scripts and algorithms in the Ryax platform. We handle the rest: building, packaging, deploying, updating…
Event based: Ryax’s workflows react instantaneously to any events and even combine events with streaming operators
Built for heterogeneous infrastructures: easy processing placement across complex IT infrastructures, transparent data transfers between modules…
- A first tour of the platform
- Running existing workflows
- Assembling new workflows
- Committing your own modules and using them in a workflow