Tutos/notebooks

README.md

@@ -10,9 +10,9 @@ This repository contains the code for optimization and machine learning algorith
 
 ## 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 detail is in one file, so you can easily understand and modify the code.
+* 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.
+* 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.
 
 ## Install
@@ -34,3 +34,7 @@ pip install -e ".[all]"
 ## Dashboards
 The integration with WandB allows us to access live dashboards of our runs (on the cluster or not). We also upload the trained models there. You can access some of our runs at https://wandb.ai/engibench/engiopt.
 <img src="imgs/wandb_dashboard.png" alt="WandB dashboards"/>
+
+## Colab notebooks
+We have some colab notebooks that show how to use some of the EngiBench/EngiOpt features.
+* [Example hard model](https://colab.research.google.com/github/IDEALLab/EngiOpt/blob/main/example_hard_model.ipynb)