Agentomics-ML¶
Autonomous AI agent for supervised machine learning model development on omics data
What is Agentomics-ML?¶
Agentomics-ML is an autonomous AI agent that develops machine learning models for omics data. Given a dataset, it produces:
- A trained model ready to run inference on new data
- A detailed report summarizing the model development process and evaluation metrics
How It Works¶
Agentomics-ML works like an ML engineer:
- Explores data before designing a model
- Considers domain information from dataset descriptions
- Chooses proper data representation (encoding, normalization, feature selection)
- Designs and trains models, including custom neural networks
- Works iteratively, reacting to issues like overfitting and underfitting based on validation metrics
- Produces working scripts with their conda environments
Key Features¶
| Feature | Description |
|---|---|
| Any LLM | Works with OpenAI, Anthropic, OpenRouter, Codex/ChatGPT OAuth, or local models via Ollama |
| Any Dataset | Supports folder-based inputs for classification or regression tasks |
| Secure Execution | Docker containers with read-only access to code and isolated execution |
| Reproducible | Outputs include trained models, scripts, and conda environments |
Deployment¶
Agentomics runs in Docker. The Python commands launch the pre-built image and mount only the files needed by each workflow. Local LLMs remain available through Ollama running on the host.
Main Commands¶
| Command | Purpose |
|---|---|
agentomics-run |
Run the full agent workflow |
agentomics-retrain |
Re-train a model with new data |
agentomics-inference |
Run predictions on new data |
agentomics-check-dataset |
Validate a dataset's format before a run |
agentomics-add-supplementary |
Attach foundation model documentation to a dataset |
Quick Example¶
The agent will guide you through selecting a model, dataset, and run parameters interactively.
License¶
MIT. See the LICENSE.