harbor-datasets-mlab
Harbor-format MLAB benchmark (MLAgentBench, arXiv:2310.03302): 9 real ML tasks across tabular / text / vision / graph / segmentation (fathomnet + identify-contrails are ≤5 GB competition subsets, the rest full). A general benchmark for
any Harbor agent — tasks are agent-neutral and self-contained.
registry.json at the root, one folder per task.
What each task contains
datasets/mlab-real/<task>/
environment/Dockerfile # builds FROM python:3.12-slim — no external base image
environment/requirements.txt
environment/prepare.py # stages the data into /workspace at build time
environment/data/ # the real data
instruction.md # task description + submission format (agent-neutral)
tests/ # scorer + verifier
task.toml # Harbor task config
Nothing outside this repository is required: no private images, no extra repos,
no tokens. docker build pulls only python:3.12-slim and public PyPI/apt
packages. The agent is installed by Harbor itself, so any agent works.
Tasks (-i): spaceship-titanic, house-price, amp-parkinsons, imdb, feedback, cifar10, ogbn-arxiv, fathomnet, identify-contrails.
Run
REPO=https://huggingface.co/datasets/danil-e/harbor-datasets-mlab
harbor run --repo $REPO -d mlab-real -i feedback -a <agent> -m <model>
The task image is built on first run (installs the task's Python stack, so the first build takes a few minutes; later runs reuse the cached image).
Harbor's other environment backends (-e ..., e.g. for hosts without a Docker
daemon) work the same way — see the Harbor documentation for their requirements.