| # 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. |
|
|
