EngiOpt

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Learning and optimization algorithms compatible with EngiBench

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# EngiOpt

This repository contains the code for optimization and machine learning algorithms for engineering design problems. Our goal here is to provide clean example usage of [EngiBench](https://github.com/IDEALLab/EngiBench) and provide strong baselines for future comparisons.

## Coding Philosophy
As much as we can, we follow the [CleanRL](https://github.com/vwxyzjn/cleanrl) philosophy: single-file, high-quality implementations with research-friendly features:
* Single-file implementation: every training detail is in one file, so you can easily understand and modify the code. There is usually another file that contains evaluation code.
* High-quality: we use type hints, docstrings, and comments to make the code easy to understand. We also rely on linters for formatting and checking our code.
* Logging: we use experiment tracking tools like [Weights & Biases](https://wandb.ai/site) to log the results of our experiments. All our "official" runs are logged in the [EngiOpt project](https://wandb.ai/engibench/engiopt).
* Reproducibility: we seed all the random number generators, make PyTorch deterministic, report the hyperparameters and code in WandB.

## Implemented algorithms

**Algorithm** | **Class** | **Dimensions** | **Conditional?** | **Model**
--- | --- | --- | --- | ---
[cgan_1d](engiopt/cgan_1d/) | Inverse Design | 1D | ✅ | GAN MLP
[cgan_2d](engiopt/cgan_2d/) | Inverse Design | 2D | ✅ | GAN MLP
[cgan_bezier](engiopt/cgan_bezier/) | Inverse Design | 1D | ✅ | GAN + Bezier layer
[cgan_cnn_2d](engiopt/cgan_cnn_2d/) | Inverse Design | 2D | ✅ | GAN + CNN
[cgan_cnn_3d](engiopt/cgan_cnn_3d/) | Inverse Design | 3D | ✅ | GAN + 3D CNN
[cgan_vae](engiopt/cgan_vae/) | Inverse Design | 3D | ✅ | MultiView GAN + VAE
[diffusion_1d](engiopt/diffusion_1d/) | Inverse Design | 1D | ❌ | Diffusion
[diffusion_2d_cond](engiopt/diffusion_2d_cond/) | Inverse Design | 2D | ✅ | Diffusion
[gan_1d](engiopt/gan_1d/) | Inverse Design | 1D | ❌ | GAN MLP
[gan_2d](engiopt/gan_2d/) | Inverse Design | 2D | ❌ | GAN MLP
[gan_bezier](engiopt/gan_bezier/) | Inverse Design | 1D | ❌ | GAN + Bezier layer
[gan_cnn_2d](engiopt/gan_cnn_2d/) | Inverse Design | 2D | ❌ | GAN + CNN
[surrogate_model](engiopt/surrogate_model/) | Surrogate Model | 1D | ❌ | MLP
[vqgan](engiopt/vqgan) | Inverse Design | 2D | ✅ | VQVAE + Transformer
[pixel_cnn_pp_2d](engiopt/pixel_cnn_pp_2d) | Inverse Design | 2D | ✅ | PixelCNN++ Autoregressive Model

## Dashboards
The integration with WandB allows us to access live dashboards of our runs (on the cluster or not). New checkpoint packages are stored on the Hugging Face Hub by default, while WandB keeps experiment tracking, metadata, and links back to the canonical checkpoint location. Historical WandB model artifacts remain supported for backward compatibility. You can access some of our runs at https://wandb.ai/engibench/engiopt.
<img src="imgs/wandb_dashboard.png" alt="WandB dashboards"/>

## Install
Install EngiOpt dependencies:
```
cd EngiOpt/
pip install -e .
```

You might want to install a specific PyTorch version, e.g., with CUDA on top of it, see [PyTorch install](https://pytorch.org/get-started/locally/).

If you're modifying EngiBench, you can install it from source and as editable:
```
git clone git@github.com:IDEALLab/EngiBench.git
cd EngiBench/
pip install -e ".[all]"
```

## Running the code

First, if you want to use weights and biases, you need to set the `WANDB_API_KEY` environment variable. You can get your API key from [wandb](https://wandb.ai/site). Then, you can run:
```
wandb login
```

If you want to save or load checkpoints from Hugging Face Hub, make sure your environment is authenticated there as well:
```
huggingface-cli login
```

### Inverse design
Usually, we provide two scripts per algorithm: one to train the model, and one to evaluate it.

To train a model, you can run (for example):

```
python engiopt/cgan_cnn_2d/cgan_cnn_2d.py --problem-id "beams2d" --track --wandb-entity None --save-model --n-epochs 200 --seed 1
```

This trains a CGAN 2D w/ CNN on `beams2d`. The flags mirror W&B's: `--track` enables W&B logging, `--wandb-entity`/`--wandb-project` say where the run goes, `--save-model` uploads the checkpoint to HuggingFace, and `--hf-entity`/`--hf-repo-prefix` say where the checkpoint goes.

W&B holds media, scalars, and run history. HuggingFace holds the model weights. One log-in per service:

```
wandb login              # for tracking
huggingface-cli login    # for checkpoints (or: export HF_TOKEN=...)
```

The defaults (`--hf-entity IDEALLab --hf-repo-prefix engiopt`) push to `huggingface.co/IDEALLab/engiopt-cgan-cnn-2d/beams2d/seed_1/`. The W&B run summary records the HF path for traceability. Each checkpoint package contains the model files plus `run_config.json` and `metadata.json`, so evaluation needs no live W&B state.

For reproducible debugging runs, you can additionally enable strict deterministic mode:
```
python engiopt/cgan_cnn_2d/cgan_cnn_2d.py --problem-id "beams2d" --seed 1 --strict-determinism
```

For new cGAN density-field runs, you can emit designs natively in the EngiBench `[0, 1]` density range while preserving older `tanh` checkpoint behavior by default:
```
python engiopt/cgan_cnn_2d/cgan_cnn_2d.py --problem-id "beams2d" --generator-output-activation sigmoid
```

Then evaluate:
```
python engiopt/cgan_cnn_2d/evaluate_cgan_cnn_2d.py --problem-id "beams2d" --seed 1 --n-samples 10
```
Evaluation pulls the checkpoint from HF automatically. For runs trained before the HF cutover, evaluation transparently falls back to the legacy W&B artifact. Pass `--hf-entity` / `--hf-repo-prefix` to point at a different HF repo.

### Surrogate model

The current surrogate model comprises several steps:
- hyperparameter tuning,
- training a (ensemble) model,
- optimization, and
- evaluation.

See this [notebook](https://github.com/IDEALLab/EngiOpt/blob/main/engiopt/surrogate_model/case_study_pe_notebook.ipynb) for an example.

Surrogate-model optimization paths now use the same checkpoint abstraction. For example, the power-electronics optimizer can consume:
* legacy WandB artifact refs
* HF package refs such as `hf://IDEALLab/engiopt-mlp-tabular-only/power_electronics/DcGain/seed_42`
* local checkpoint package directories

For migration guidance on moving historical checkpoint subsets from WandB to the IDEALLab HF organization later, see [docs/checkpoint_migration_playbook.md](docs/checkpoint_migration_playbook.md).

## Colab notebooks
We have some colab notebooks that show how to use some of the EngiBench/EngiOpt features.
* [Example easy model (GAN)](https://colab.research.google.com/github/IDEALLab/EngiOpt/blob/main/example_easy_model.ipynb)
* [Example hard model (Diffusion)](https://colab.research.google.com/github/IDEALLab/EngiOpt/blob/main/example_hard_model.ipynb)

## Citing

If you use EngiBenc/EngiOpt in your research, please cite the following paper:

```bibtex
@misc{felten_engibench_2025,
	title = {{EngiBench}: {A} {Framework} for {Data}-{Driven} {Engineering} {Design} {Research}},
	url = {http://arxiv.org/abs/2508.00831},
	doi = {10.48550/arXiv.2508.00831},
	urldate = {2025-08-07},
	publisher = {arXiv},
	author = {Felten, Florian and Apaza, Gabriel and B\¨aunlich, Gerhard and Diniz, Cashen and Dong, Xuliang and Drake, Arthur and Habibi, Milad and Hoffman, Nathaniel J. and Keeler, Matthew and Massoudi, Soheyl and VanGessel, Francis G. and Fuge, Mark},
	month = jun,
	year = {2025},
}
```

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