| 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. | |