Introduction¶

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

ado is a unified platform for executing computational experiments at scale and analysing their results. It can be extended with new experiments or new analysis tools. It allows distributed teams of researchers and engineers to collaborate on projects, execute experiments, and share data.

You can run the experiments and analysis tools already available in ado in a distributed, shared, environment with your team. You can also use ado to get features like data-tracking, data-sharing, tool integration and a CLI, for your analysis method or experiment for free.

🧑‍💻 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

Foundation Model Experimentation¶

We have developed ado plugins providing advanced experiments for testing foundation-models:

• fine-tuning performance benchmarking
• inference performance benchmarking (using the vLLM performance benchmark)
• COMING SOON inference and fine-tuning prediction

Requirements¶

A basic installation of ado only requires a recent Python version (3.10+). 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¶