---
dataset_info:
  features:
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: ground_truth
    dtype: string
  - name: dataset
    dtype: string
  - name: env_config
    struct:
    - name: env_name
      dtype: string
    - name: image
      dtype: string
    - name: task_id
      dtype: string
  - name: source
    dtype: string
  splits:
  - name: train
    num_bytes: 21182796
    num_examples: 4567
  download_size: 20865488
  dataset_size: 21182796
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
language:
- en
tags:
- tmax
- terminal
- agents
- reinforcement-learning
---

# SETA-Env for tmax

**Images require building:** the dataset conversion and complete task archive are published. 0 task image(s) were published during preparation; the remaining images are intentionally left for the included resumable build script. No build job is running for this release. Only tasks with available images can run immediately. See `image-build-status.json` for the publication-time snapshot.

All **4,567** task directories from [camel-ai/SETA-Env](https://huggingface.co/datasets/camel-ai/SETA-Env), pinned to `3c3bc8975b05826bf41769bd6b2da76ad1b6dd42`, converted to tmax's `swerl_vanillux_sandbox` format.

The train split uses the same `messages`, `ground_truth`, `dataset`, `env_config`, and `source` schema as the other `hamishivi/agent-task-*` datasets. Messages use the tmax Vanillux templates; `dataset` is `passthrough`. Task IDs are prefixed with `seta_env__` to avoid collisions when combining task archives. `task-manifest.json` maps them back to original paths.

`task-data.tar.gz` contains a directory per task with all original task files preserved byte-for-byte, plus `image.txt`. Instructions, verifiers, build contexts, metadata, and reference solutions are retained. The tmax loader exposes only environment seed files and defers tests until submission; reference solutions are not mounted into the agent sandbox.

## Usage

```python
from datasets import load_dataset
from huggingface_hub import hf_hub_download

train = load_dataset("hamishivi/agent-task-seta-env", split="train")
archive = hf_hub_download("hamishivi/agent-task-seta-env", "task-data.tar.gz", repo_type="dataset")
```

Extract the archive and set the sandbox `task_data_dir` to the directory containing the task directories. Every row specifies its image explicitly. Task resource requirements and verifier timeouts remain in the original `task.toml`; configure the training harness accordingly.

## Images

Images use `hamishi740/agent-task-seta-env:<source-environment-hash>` and target Linux AMD64. See `task-manifest.json` for build status.

## Validation and attribution

All rows passed schema and Parquet round-trip checks, and every archived task file was checked against the downloaded source bytes. See `validation.json`. This does not constitute an evaluation of every task or a full training run.

The upstream license is apache-2.0. Consult the upstream dataset card for citation and provenance. tmax Vanillux prompts are adapted from mini-swe-agent (MIT).

## Resume image builds

Requires Docker Buildx and a Docker Hub login with write access to the `hamishi740` target repositories. The script skips tags already published, retains per-image logs, and can be rerun after failures.

```sh
mkdir -p tasks
tar -xzf task-data.tar.gz -C tasks
python build_dataset_images.py --task-data-dir tasks --manifest task-manifest.json --workers 8 --timeout 1800
```

To use another Docker Hub namespace, update the image references consistently in the manifest, training records, and task `image.txt` files before building.

Runtime smoke tests were not run for SETA because image builds were deferred. Structural, schema, and archive checks passed for all tasks.

## Apptainer images

The current Apptainer pool and unified download manifest are maintained in [TMaxxx/agent-task-seta-env](https://huggingface.co/datasets/TMaxxx/agent-task-seta-env). New SIF uploads go to TMaxxx; earlier images remain available here. Use the downloader and manifest in the linked repository to retrieve all available images with tmax-compatible filenames.