| Name | ado-core JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | None |
| Summary | ado is a unified platform for executing computational experiments at scale and analysing their results. It can be easily 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. |
| upload_time | 2025-09-01 11:27:37 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.10 |
| license | None |
| keywords |
discovery
exploration
optimization
orchestration
ray
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
|
# Introduction
This is the repository for the **a**ccelerated **d**iscovery **o**rchestrator (**`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](https://clig.dev)
* 🤝 *Projects*: Allow distributed groups of users to [collaborate and share data](https://ibm.github.io/ado/resources/metastore)
* 🔌 *Extendable*: Easily [add new experiments](https://ibm.github.io/ado/actuators/creating-custom-experiments), [optimizers or other tools.](https://ibm.github.io/ado/operators/creating-operators)
* ⚙️ *Scalable*: We use [ray](https://ray.io) as our execution engine allowing experiments and tools to easily scale
* ♻️ *Automatic data-reuse*: Avoid repeating work with [transparent reuse of experiment results](https://ibm.github.io/ado/core-concepts/data-sharing). `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](https://ibm.github.io/ado/getting-started/ado#ado-show-related)
* 🔎 *Optimization and sampling*: Out-of-the-box, leverage powerful optimization methods [via `raytune`](https://ibm.github.io/ado/operators/optimisation-with-ray-tune) or use our [flexible in built sampler](https://ibm.github.io/ado/operators/random-walk)
### Foundation Model Experimentation
We have developed `ado` plugins providing advanced experiments for testing foundation-models:
* ⏱️ [fine-tuning performance benchmarking ](https://ibm.github.io/ado/actuators/sft-trainer)
* ⏱️ inference performance benchmarking (using the [vLLM performance benchmark](https://docs.vllm.ai/en/stable/api/vllm/benchmarks/serve.html))
* **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](https://ibm.github.io/ado/examples/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](https://ibm.github.io/ado/getting-started/installing-backend-services#using-the-distributed-mysql-backend-for-ado) for more
* **Multi-Node Execution** **_(Optional)_**: To support multi-node or scaling execution you may need a multi-node RayCluster. See [here](https://ibm.github.io/ado/getting-started/installing-backend-services#deploying-kuberay-and-creating-a-raycluster) 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](https://ibm.github.io/ado/actuators/sft-trainer#configure-your-raycluster)
* **_vLLM Performance Benchmarking_**: Requires an OpenShift cluster with GPUs
## Install
To install you can execute the following (we recommend you set up a virtual environment)
```commandline
git clone https://github.com/IBM/ado.git
cd ado
pip install .
```
Alternate instructions to install `ado` can be found
here: https://ibm.github.io/ado/getting-started/install/
## Development
Instructions for developing ado are available in [DEVELOPING](DEVELOPING.md).
### Testing
To run unit-tests read [tests/README.md](tests/README.md).
## Example
This video shows listing [actuators](website/docs/actuators/working-with-actuators.md) and getting the details of an experiment.
Check [demo](https://ibm.github.io/ado/getting-started/demo) for more videos.
[](https://github.com/user-attachments/assets/fc4862f3-763b-4967-ab3c-4bd359900a50)
## Technical Report
For more details on the Discovery Spaces concept underlying ado, please refer to this [technical report](https://arxiv.org/abs/2506.21467)
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"description": "# Introduction\n\nThis is the repository for the **a**ccelerated **d**iscovery **o**rchestrator (**`ado`**). \n\n**`ado`** is a unified platform for **executing computational experiments at scale** and **analysing their results**.\nIt can be extended with new experiments or new analysis tools. \nIt allows distributed teams of researchers and engineers to collaborate on projects, execute experiments, and share data.\n\nYou can run the experiments and analysis tools already available in **`ado`** in a distributed, shared, environment with your team.\nYou 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.\n\n\ud83e\uddd1\u200d\ud83d\udcbb Using **`ado`** assumes familiarity with command line tools. \n\n\ud83d\udee0\ufe0f Developing **`ado`** requires knowledge of python. \n\n## Key Features\n\n* \ud83d\udcbb *CLI*: Our human-centric CLI follows [best practices](https://clig.dev) \n* \ud83e\udd1d *Projects*: Allow distributed groups of users to [collaborate and share data](https://ibm.github.io/ado/resources/metastore)\n* \ud83d\udd0c *Extendable*: Easily [add new experiments](https://ibm.github.io/ado/actuators/creating-custom-experiments), [optimizers or other tools.](https://ibm.github.io/ado/operators/creating-operators)\n* \u2699\ufe0f *Scalable*: We use [ray](https://ray.io) as our execution engine allowing experiments and tools to easily scale\n* \u267b\ufe0f *Automatic data-reuse*: Avoid repeating work with [transparent reuse of experiment results](https://ibm.github.io/ado/core-concepts/data-sharing). `ado` internal protocols ensure this happens only when it makes sense \n* \ud83d\udd17 *Provenance*: As you work, the relationship between the data you create and operations you perform are [automatically tracked](https://ibm.github.io/ado/getting-started/ado#ado-show-related)\n* \ud83d\udd0e *Optimization and sampling*: Out-of-the-box, leverage powerful optimization methods [via `raytune`](https://ibm.github.io/ado/operators/optimisation-with-ray-tune) or use our [flexible in built sampler](https://ibm.github.io/ado/operators/random-walk) \n\n### Foundation Model Experimentation\n\nWe have developed `ado` plugins providing advanced experiments for testing foundation-models:\n\n* \u23f1\ufe0f [fine-tuning performance benchmarking ](https://ibm.github.io/ado/actuators/sft-trainer)\n* \u23f1\ufe0f inference performance benchmarking (using the [vLLM performance benchmark](https://docs.vllm.ai/en/stable/api/vllm/benchmarks/serve.html))\n* **COMING SOON** \ud83d\udd2e inference and fine-tuning prediction \n\n## Requirements\n\nA basic installation of `ado` only requires a recent Python version (3.10+). This will allow you to run [many of our examples](https://ibm.github.io/ado/examples/examples) and explore ado features.\n\n### Additional Requirements\n\nSome advanced features have additional requirements:\n\n* **Distributed Projects** **_(Optional)_**: To support projects with multiple users you will need a remote, accessible, MySQL database. See [here](https://ibm.github.io/ado/getting-started/installing-backend-services#using-the-distributed-mysql-backend-for-ado) for more\n* **Multi-Node Execution** **_(Optional)_**: To support multi-node or scaling execution you may need a multi-node RayCluster. See [here](https://ibm.github.io/ado/getting-started/installing-backend-services#deploying-kuberay-and-creating-a-raycluster) for more details\n\nIn addition `ado` plugins may have additional requirements for executing **_realistic_** experiments. For example,\n\n* **_Fine-Tuning Benchmarking_**: Requires a [RayCluster with GPUs](https://ibm.github.io/ado/actuators/sft-trainer#configure-your-raycluster)\n* **_vLLM Performance Benchmarking_**: Requires an OpenShift cluster with GPUs \n## Install\n\nTo install you can execute the following (we recommend you set up a virtual environment)\n```commandline\ngit clone https://github.com/IBM/ado.git\ncd ado\npip install .\n```\n\nAlternate instructions to install `ado` can be found\nhere: https://ibm.github.io/ado/getting-started/install/\n\n## Development\n\nInstructions for developing ado are available in [DEVELOPING](DEVELOPING.md).\n\n### Testing\n\nTo run unit-tests read [tests/README.md](tests/README.md).\n\n## Example\n\nThis video shows listing [actuators](website/docs/actuators/working-with-actuators.md) and getting the details of an experiment.\n\nCheck [demo](https://ibm.github.io/ado/getting-started/demo) for more videos.\n\n\n[](https://github.com/user-attachments/assets/fc4862f3-763b-4967-ab3c-4bd359900a50)\n\n\n## Technical Report\n\nFor more details on the Discovery Spaces concept underlying ado, please refer to this [technical report](https://arxiv.org/abs/2506.21467)\n",
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