biotaatlas

Run your own atlas

biota is a self-contained CLI tool. You can run it on a laptop for quick iteration or across a Ray cluster for full-scale searches. The only hard dependencies are Python 3.12, PyTorch, and Ray.

Install

# Clone and install
git clone https://github.com/rkv0id/biota
cd biota
uv sync

Quick start

uv run biota search --preset dev --budget 200

python scripts/build_index.py
open runs/index.html

GPU and cluster

On Apple Silicon pass --device mps --batch-size 32 for a meaningful speedup. On a CUDA cluster, use --batch-size 64 --workers N where N is the number of nodes. The standard preset (192×192, 300 steps) at B=64 on an RTX 5060 Ti cluster runs 500 rollouts in about 97 seconds.

# Apple Silicon
uv run biota search --preset standard --budget 500 \
    --device mps --batch-size 32

# Single CUDA GPU, no Ray
uv run biota search --preset standard --budget 500 \
    --device cuda --batch-size 64

# Multi-node Ray cluster, custom descriptor axes
uv run biota search --ray-address head:6379 \
    --preset standard --budget 2000 \
    --device cuda --batch-size 64 --workers 3 \
    --descriptors oscillation,compactness,png_compressibility

Choosing descriptors

Pass --descriptors with three comma-separated names to control which behavioral axes the archive uses. With 9 built-ins there are 84 possible configurations. You can also supply your own via --descriptor-module path/to/file.py — the file must define a list named DESCRIPTORS containing Descriptor objects.

# Default axes
uv run biota search --descriptors velocity,gyradius,spectral_entropy

# Shape and complexity axes
uv run biota search --descriptors oscillation,compactness,png_compressibility

More detail