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, 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
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.
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. 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.