Add cuml ray hpo example

source/examples/cuml-ray-hpo/setup/env.yaml

@@ -0,0 +1,14 @@
+name: ray-cuml
+channels:
+  - rapidsai
+  - conda-forge
+dependencies:
+  - python=3.13
+  - "ray-default=2.53.0"
+  - "ray-data=2.53.0"
+  - "ray-train=2.53.0"
+  - "ray-tune=2.53.0"
+  - cuml=26.02
+  - "cuda-version=13.0"
+  - ipykernel
+  - ipywidgets

source/examples/cuml-ray-hpo/setup/get_data.py

@@ -0,0 +1,71 @@
+import argparse
+import os
+from urllib.request import urlretrieve
+
+# If script is in setup/, use parent directory; otherwise use script directory or cwd
+_script_dir = os.path.dirname(os.path.abspath(__file__))
+if os.path.basename(_script_dir) == "setup":
+    # Script is in setup/ directory, use parent directory
+    _data_dir = os.path.join(os.path.dirname(_script_dir), "data")
+else:
+    # Script is not in expected location, use current working directory
+    _data_dir = os.path.join(os.getcwd(), "data")
+
+
+def prepare_dataset(use_full_dataset=False):
+    """
+    Download the airline dataset.
+
+    Parameters
+    ----------
+    use_full_dataset : bool, default=False
+        If True, downloads the full dataset (20M rows).
+        If False, downloads the small dataset.
+    """
+    data_dir = _data_dir
+
+    # Set filename based on dataset size
+    if use_full_dataset:
+        file_name = "airlines.parquet"
+        url = "https://data.rapids.ai/cloud-ml/airline_20000000.parquet"
+    else:
+        file_name = "airlines_small.parquet"
+        url = "https://data.rapids.ai/cloud-ml/airline_small.parquet"
+
+    parquet_name = os.path.join(data_dir, file_name)
+
+    if os.path.isfile(parquet_name):
+        print(f" > File already exists. Ready to load at {parquet_name}")
+    else:
+        # Ensure folder exists
+        os.makedirs(data_dir, exist_ok=True)
+
+        def data_progress_hook(block_number, read_size, total_filesize):
+            if (block_number % 1000) == 0:
+                print(
+                    f" > percent complete: { 100 * ( block_number * read_size ) / total_filesize:.2f}\r",
+                    end="",
+                )
+            return
+
+        urlretrieve(
+            url=url,
+            filename=parquet_name,
+            reporthook=data_progress_hook,
+        )
+
+        print(f" > Download complete {file_name}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Download airline dataset for cuML Ray HPO example"
+    )
+    parser.add_argument(
+        "--full-dataset",
+        action="store_true",
+        help="Download the full dataset (20M rows) instead of the small dataset",
+    )
+    args = parser.parse_args()
+
+    prepare_dataset(use_full_dataset=args.full_dataset)

source/examples/cuml-ray-hpo/setup/pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "ray-cuml"
+version = "0.1.0" 
+requires-python = "==3.13.*"
+dependencies = [
+    "ray[default]==2.53.0",
+    "ray[data]==2.53.0",
+    "ray[train]==2.53.0",
+    "ray[tune]==2.53.0",
+    "cuml-cu12==26.2.*",
+    "jupyterlab-nvdashboard",
+    "ipykernel",
+    "ipywidgets"
+]

source/examples/index.md

@@ -25,4 +25,5 @@ cuml-snowflake-nb/notebook
 rapids-coiled-cudf/notebook
 rapids-morpheus-pipeline/notebook
 lulc-classification-gpu/notebook
+cuml-ray-hpo/notebook
 ```