Introduction¶

This is the webpage for the accelerated discovery orchestrator (ado).

ado is a Python platform for designing experiment campaigns and executing them at scale. Built to address recurring challenges in research software development, ado empowers distributed teams of researchers and engineers to seamlessly collaborate, execute experiments, and share data.

You can extend ado across different domains through its lightweight plugin model-often as simple as decorating a Python function. By integrating your methodology, you gain powerful cross-cutting capabilities—such as parallel execution, data provenance, and a unified CLI—alongside a structured foundation that allows AI coding agents to autonomously formulate and run your experiments.

🧑‍💻 Using ado assumes familiarity with command line tools.

🛠️ Developing ado requires knowledge of python.

Key Features¶

• CLI: Our human-centric CLI follows best practices
• Projects: Allow distributed groups of users to collaborate and share data
• 🔌 Extendable: Easily add new experiments, optimizers or other tools.
• Scalable: We use ray as our execution engine allowing experiments and tools to easily scale
• Automatic data-reuse: Avoid repeating work with transparent reuse of experiment results. ado internal protocols ensure this happens only when it makes sense
• Provenance: As you work, the relationship between the data you create and operations you perform are automatically tracked
• Optimization and sampling: Out-of-the-box, leverage powerful optimization methods via raytune or use our flexible in built sampler
• Coding agents: Supercharge your workflow. ado's typed resources and bundled skills enable AI assistants to autonomously formulate, validate, and run experiments. Learn more.

Foundation Model Experimentation¶

We have developed ado plugins providing advanced capabilities for performance testing of foundation-models:

• fine-tuning performance benchmarking
• inference performance benchmarking (using vLLM bench or guidellm)
• predictive performance models creation

Requirements¶

A basic installation of ado only requires a recent Python version (3.10 to 3.13). This will allow you to run many of our examples and explore ado features.

Additional Requirements¶

Some advanced features have additional requirements:

• Distributed Projects (Optional): To support projects with multiple users you will need a remote, accessible, MySQL database. See here for more
• Multi-Node Execution (Optional): To support multi-node or scaling execution you may need a multi-node RayCluster. See here for more details

In addition ado plugins may have additional requirements for executing realistic experiments. For example,

• Fine-Tuning Benchmarking: Requires a RayCluster with GPUs
• vLLM Performance Benchmarking: Requires an OpenShift cluster with GPUs

Try it out¶

Example¶

This video shows listing actuators and getting the details of an experiment. Check demo for more videos.

Acknowledgement¶

This project is partially funded by the European Union through the Smart Networks and Services Joint Undertaking (SNS JU) under grant agreement No. 101192750 (Project 6G-DALI).

What's next¶