Add installation guide and migration guide

docs/source/fil_migration.rst

@@ -0,0 +1,63 @@
+###############
+Migration guide
+###############
+
+Basic workflow
+==============
+
+.. testsetup:: workflow
+
+    import numpy as np
+    from sklearn.ensemble import RandomForestClassifier
+
+    X = np.array([[1, 2], [-1, 2]], dtype="float32")
+    y = np.array([0, 1], dtype="int32")
+
+    skl_model = RandomForestClassifier(n_estimators=1, max_depth=1)
+    skl_model.fit(X, y)
+
+Call :py:meth:`~nvforest.load_model`, :py:meth:`~nvforest.load_from_sklearn`,
+or :py:meth:`~nvforest.load_from_treelite_model`. Note that it is no longer
+necessary to specify the ``is_classifier`` parameter.
+
+.. code-block:: python
+
+    # BEFORE
+    import cuml
+    fil_model = cuml.fil.ForestInference.load_from_sklearn(skl_model, is_classifier=True)
+    fil_model.optimize(batch_size=1024)
+    predictions = fil_model.predict(X)
+    probabilities = fil_model.predict_proba(X)
+    per_tree_pred = fil_model.predict_per_tree(X)
+    lead_ids = fil_model.apply(X)
+
+.. testcode:: workflow
+
+    # AFTER
+    import nvforest
+    nvforest_model = nvforest.load_from_sklearn(skl_model)
+    nvforest_model = nvforest_model.optimize(batch_size=1024)
+    predictions = nvforest_model.predict(X)
+    probabilities = nvforest_model.predict_proba(X)
+    per_tree_pred = nvforest_model.predict_per_tree(X)
+    lead_ids = nvforest_model.apply(X)
+
+Device selection
+================
+Specify the ``device`` parameter when calling :py:meth:`~nvforest.load_model`.
+
+.. code-block:: python
+
+    # BEFORE
+    with cuml.fil.set_fil_device_type("cpu"):
+        fil_model = cuml.fil.ForestInference.load_from_sklearn(skl_model)
+
+.. testcode:: workflow
+
+    # AFTER
+    nvforest_model = nvforest.load_from_sklearn(skl_model, device="cpu")
+
+nvForest also differs from FIL when it comes to the behavior when no device is explicitly
+specified. The ``device`` parameter defaults to ``"auto"``. nvForest will attempt to
+load the tree model onto a GPU device, if one is available. If no GPU is available,
+nvForest will fall back to the CPU.

docs/source/getting_started.rst

@@ -2,6 +2,23 @@
 Getting started with nvForest
 #############################
 
+Installation
+============
+You can install nvForest using Pip or Conda.
+
+.. code-block:: console
+
+   # Using Pip: need a suffix corresponding to your CUDA version, e.g. for CUDA 13:
+   $ pip install nvforest-cu13
+
+.. code-block:: console
+
+   # Using Conda: need to specify the rapidsai channel
+   $ conda install -c rapidsai -c conda-forge nvforest
+
+You can also install nvForest as part of RAPIDS, a collection of libraries for GPU accelerated data science.
+Visit https://docs.rapids.ai/install/ for more information.
+
 nvForest with Python
 ====================
 

docs/source/index.rst

@@ -8,6 +8,7 @@ It supports many kinds of decision tree models, including XGBoost, LightGBM, sci
 * :doc:`python_api`: Python API documentation
 * :doc:`cpp_api`: C++ API documentation
 * :doc:`build`: How to build nvForest from the source
+* :doc:`fil_migration`: How to migrate existing code using Forest Inference Library (FIL)
 
 .. toctree::
     :maxdepth: 2
@@ -17,3 +18,4 @@ It supports many kinds of decision tree models, including XGBoost, LightGBM, sci
     python_api
     cpp_api
     build
+    fil_migration