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Installation

Agentomics-ML runs in Docker. Everything—including Conda environments, dataset preparation, and agent-generated code—runs inside the container. You drive it through the installed agentomics-run command, which launches the container, mounts the files each workflow needs, and collects the results.

Requirements

  • Docker installed and running
  • Python 3.11+ to install the CLI

Setup

Install the CLI from PyPI, then run it. No repository clone is required — the container image is pulled automatically on the first run:

python3 -m pip install agentomics

# Provide at least one provider key, exported or in a ./.env file, e.g.:
export OPENROUTER_API_KEY=...

agentomics-run --dataset my_dataset

With no run arguments, the run is interactive, prompting for model, dataset, and iterations.

The launcher takes care of the container plumbing for you:

  • Datasets are read from ./datasets; select one with --dataset <name>.
  • Results are written to ./outputs/<agent_id>/ (a fresh directory per run).
  • Output files are owned by you, not root.
  • A .env file in the current directory and exported provider keys are passed through automatically.
  • GPU access is enabled by default; pass --cpu-only to disable it (see GPU Settings).
  • For the codex provider, ~/.codex is mounted read-only automatically.

By default the launcher uses the image matching the installed package version: biogemt/agentomics:<installed-package-version>. Use --image <name> only to select another image explicitly, such as a locally built development image.

Building the image yourself

git clone https://github.com/BioGeMT/Agentomics-ML.git
cd Agentomics-ML
docker build -t agentomics .

The build uses the repository's main branch by default. To build the image from another branch, pass its Git URL and branch with REPOSITORY_SOURCE:

docker build \
  --build-arg REPOSITORY_SOURCE=https://github.com/BioGeMT/Agentomics-ML.git#my-branch \
  -t agentomics .

Contributors testing changes from a working tree should use --dev; see Local Development.

Ollama (Local LLMs)

Run with local models using Ollama for privacy or offline use.

Requirements

  • Ollama installed and running on the host

Set OLLAMA_BASE_URL and select the ollama provider. The launcher enables host networking automatically so the container can reach the Ollama server:

export OLLAMA_BASE_URL=http://localhost:11434/v1
agentomics-run --provider ollama --model <ollama-model> --dataset <dataset>

CPU-Only Mode

Disable GPU acceleration with --cpu-only:

agentomics-run --cpu-only

Next Steps