feat: support for rapids singlecell in scvi-tools (#3811)

Author: ori-kron-wis

CHANGELOG.md

@@ -3,6 +3,20 @@
 Starting from version 0.20.1, this format is based on [Keep a Changelog], and this project adheres
 to [Semantic Versioning]. The full commit history is available in the [commit logs](https://github.com/scverse/scvi-tools/commits/).
 
+## Version 1.5
+
+### 1.5.0 (2026-XX-XX)
+
+#### Added
+
+- Add support for rapids-singlecell, {pr}`3811`.
+
+#### Fixed
+
+#### Changed
+
+#### Removed
+
 ## Version 1.4
 
 ### 1.4.3 (2026-05-12)

Dockerfile

@@ -1,8 +1,6 @@
 FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04
 FROM python:3.12 AS base
 
-ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/nvidia/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}
-
 RUN pip install --no-cache-dir uv
 
 RUN uv pip install --system --no-cache torch torchvision torchaudio

pyproject.toml

@@ -59,7 +59,8 @@ tests = ["pytest", "pytest-pretty", "coverage", "scvi-tools[optional]"]
 editing = ["jupyter", "pre-commit"]
 dev = ["scvi-tools[editing,tests]"]
 test = ["scvi-tools[tests]"]
-cuda = ["torchvision", "torchaudio", "jax[cuda12]","mlx[cuda]"]
+cuda = ["torchvision", "torchaudio", "jax[cuda]", "mlx[cuda]"]
+cuda13 = ["torchvision", "torchaudio", "jax[cuda13]", "mlx[cuda13]"]
 tpu = ["torch_xla[tpu]"]
 metal = ["torchvision", "torchaudio", "jax-metal","mlx-metal"]
 
@@ -79,8 +80,8 @@ docs = [
 ]
 docsbuild = ["scvi-tools[docs,autotune,hub,jax,diagvi]","mlx"]
 
-# scvi.autotune #TODO remove ray[tune] constraint once solved
-autotune = ["hyperopt>=0.2", "ray[tune]; python_version < '3.14'", "scib-metrics", "muon"]
+# scvi.autotune
+autotune = ["hyperopt>=0.2", "ray[tune]", "scib-metrics", "muon"]
 # scvi.hub dependencies
 hub = ["huggingface_hub", "dvc[s3]", "boto3"]
 # scvi.data.add_dna_sequence
@@ -99,6 +100,9 @@ dataloaders = ["lamindb>=1.12.1", "cellxgene-census", "tiledbsoma", "tiledbsoma_
 diagvi = ["torch_geometric", "geomloss"]
 # for mlflow
 mlflow = ["mlflow","psutil","GPUtil","nvidia-ml-py"]
+# for rapids
+rapids = [ "cugraph>=24", "cuml>=24", "cupy-cuda12x", "rapids-singlecell[rapids]" ]
+rapids-cuda13 = [ "cugraph>=24", "cuml>=24", "cupy-cuda13x", "rapids-singlecell[rapids]" ]
 
 optional = [
     "scvi-tools[autotune,mlflow,hub,jax,file_sharing,regseq,parallel,interpretability,diagvi]",

src/scvi/criticism/_ppc.py

@@ -331,9 +331,17 @@ def differential_expression(
         sc.pp.normalize_total(adata_de, target_sum=cell_scale_factor)
         sc.pp.log1p(adata_de)
         if de_groupby is None:
-            sc.tl.pca(adata_de)
-            sc.pp.neighbors(adata_de)
-            sc.tl.leiden(adata_de, key_added="leiden_scvi_criticism")
+            try:
+                import rapids_singlecell as rsc
+
+                print("RAPIDS SingleCell is installed and can be imported")
+                rsc.pp.pca(adata_de)
+                rsc.pp.neighbors(adata_de)
+                rsc.tl.leiden(adata_de, key_added="leiden_scvi_criticism")
+            except ImportError:
+                sc.tl.pca(adata_de)
+                sc.pp.neighbors(adata_de)
+                sc.tl.leiden(adata_de, key_added="leiden_scvi_criticism")
             de_groupby = "leiden_scvi_criticism"
         with warnings.catch_warnings():
             warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning)

src/scvi/external/resolvi/_model.py

@@ -360,7 +360,7 @@ def _prepare_data(
         if slice_key is not None:
             batch_key = slice_key
         try:
-            import scanpy
+            import scanpy as sc
             from sklearn.neighbors._base import _kneighbors_from_graph
         except ImportError as err:
             raise ImportError(
@@ -381,14 +381,12 @@ def _prepare_data(
         for index in indices:
             sub_data = adata[index].copy()
             try:
-                import rapids_singlecell
+                import rapids_singlecell as rsc
 
                 print("RAPIDS SingleCell is installed and can be imported")
-                rapids_singlecell.pp.neighbors(
-                    sub_data, n_neighbors=n_neighbors + 5, use_rep=spatial_rep
-                )
+                rsc.pp.neighbors(sub_data, n_neighbors=n_neighbors + 5, use_rep=spatial_rep)
             except ImportError:
-                scanpy.pp.neighbors(sub_data, n_neighbors=n_neighbors + 5, use_rep=spatial_rep)
+                sc.pp.neighbors(sub_data, n_neighbors=n_neighbors + 5, use_rep=spatial_rep)
             distances = sub_data.obsp["distances"] ** 2
 
             distance_neighbor[index, :], index_neighbor_batch = _kneighbors_from_graph(