---
license: apache-2.0
tags:
  - docker
  - alfworld
---

# e2l-train Docker image

Prebuilt Stage-2 training image for [Ch1nyzzz/e2-learning](https://github.com/Ch1nyzzz/e2-learning).

**Code and environment are both inside the image.** You do not need to `git clone` to train.

Contents:

- `/workspace/e2-learning`: this repository (scripts, configs, `.env.example`)
- `/opt/verl-agent`: langfengQ/verl-agent at `20bd331` plus the stage-2 patch
- env A (system Python 3.12): torch 2.8.0 / vllm 0.11.0 / flash-attn 2.8.3
- env B (`/opt/venvs/rwml`, Python 3.10): torch 2.6.0 / vllm 0.8.3 / verl 0.4.1

Tag after load: `e2l-train:latest` (~46.6GB uncompressed, ~21GB this tarball).
Working directory: `/workspace/e2-learning`.

## Load

```bash
hf download erv1n/e2l-train-image --repo-type dataset --local-dir ./e2l-train-image
cd e2l-train-image
sha256sum -c e2l-train.tar.gz.sha256
gunzip -c e2l-train.tar.gz | docker load
```

Then follow the **GitHub** playbook (source of truth, may be newer than the copy inside the image):
https://github.com/Ch1nyzzz/e2-learning/blob/main/docs/playbook.md

Create a host work directory, `docker run` with those mounts, pull models, start training. Do **not** bind-mount a git checkout over `/workspace/e2-learning` or you will hide the baked-in code.