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36fa1f6 bbb6958 8d3cf93 36fa1f6 8d3cf93 36fa1f6 7994c7f 36fa1f6 bbb6958 36fa1f6 b661ee8 1aa4974 7994c7f 8d3cf93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # harbor-datasets-tabred
Harbor-format **TabReD** benchmark (yandex-research/tabred, ICLR 2025): 8 real industry tabular tasks with a temporal train/test split (40k-train / 10k-test slice). 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/tabred-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`): weather, cooking-time, delivery-eta, maps-routing, sberbank-housing, homesite-insurance, ecom-offers, homecredit-default.
## Run
```bash
REPO=https://huggingface.co/datasets/danil-e/harbor-datasets-tabred
harbor run --repo $REPO -d tabred-real -i weather -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.
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