download instructions

README.md

@@ -19,6 +19,72 @@ This dataset contains input data and solutions for small-size Optimal Power Flow
 Original case files are based on instances from Power Grid Lib -- Optimal Power Flow ([PGLib OPF](https://github.com/power-grid-lib/pglib-opf));
 this dataset comprises instances corresponding to systems with up to 300 buses.
 
+## Download instructions
+
+The recommended way to download this dataset is through the [HuggingFace client library](https://huggingface.co/docs/hub/datasets-downloading#using-the-hugging-face-client-library).
+
+### Downloading the entire dataset
+
+1. Install `huggingface_hub` (see official [installation instructions](https://huggingface.co/docs/huggingface_hub/installation))
+    ```bash
+    pip install --upgrade huggingface_hub
+    ```
+2. Download the dataset.
+    It is recommended to save files to a local directory
+    ```py
+    from huggingface_hub import snapshot_download
+    REPO_ID = "PGLearn/PGLearn-Small"
+    LOCAL_DIR = "<path/to/local/directory>"
+    snapshot_download(repo_id=REPO_ID, repo_type="dataset", local_dir=LOCAL_DIR)
+    ```
+    Note that by default, `snapshot_download` saves files to a local cache.
+3. De-compress all the files
+    ```bash
+    cd <path/to/local/directory>
+    find ./ -type f -name "*.gz" -exec unpigz -v {} +
+    ```
+
+### Downloading individual files
+
+The entire PGLearn-Small collection takes about 180GB of disk space (compressed).
+
+To avoid large disk usage and long download times, it is possible to download only a subset of the files.
+This approach is recommended for users who only require a subset of the dataset, for instance:
+* a subset of cases
+* a specific OPF formulation (e.g. only ACOPF)
+* only primal solutions (as opposed to primal and dual)
+
+This can be achieved by using the `allow_patterns` and `ignore_patterns` parameters (see [official documentation](https://huggingface.co/docs/huggingface_hub/guides/download#filter-files-to-download)),
+in lieu of step 2. above.
+
+* To download only the `14_ieee` and `30_ieee` cases:
+    ```py
+    REPO_ID = "PGLearn/PGLearn-Small"
+    CASES   = ["14_ieee", "30_ieee"]
+    LOCAL_DIR = "<path/to/local/dir>"
+    
+    snapshot_download(repo_id=REPO_ID, allow_patterns=[f"{case}/" for case in CASES], repo_type="dataset", local_dir=LOCAL_DIR)
+    ```
+* To download a specific OPF formulation
+    (the repository structure makes it simpler to exclude non-desired OPF formulations)
+    ```py
+    REPO_ID = "PGLearn/PGLearn-Small"
+    ALL_OPFS = ["ACOPF", "DCOPF", "SOCOPF"]
+    SELECTED_OPFS = ["ACOPF", "DCOPF"]
+    LOCAL_DIR = "<path/to/local/dir>"
+    
+    snapshot_download(repo_id=REPO_ID, ignore_patterns=[f"*/{opf}/*" for opf in ALL_OPFS if opf not in SELECTED_OPFS], repo_type="dataset", local_dir=LOCAL_DIR)
+    ```
+    
+* To download only primal solutions
+    ```py
+    REPO_ID = "PGLearn/PGLearn-Small"
+    LOCAL_DIR = "<path/to/local/dir>"
+    
+    snapshot_download(repo_id=REPO_ID, ignore_patterns="*dual.h5.gz", repo_type="dataset", local_dir=LOCAL_DIR)
+    ```
+
+
 ## Contents
 
 For each system (e.g., `14_ieee`, `118_ieee`), the dataset provides multiple OPF instances,