remove randomstate

.github/workflows/test-gpu-rpr.yml

@@ -0,0 +1,60 @@
+name: GPU-CI-RAPIDS-PRE
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    types:
+      - labeled
+      - opened
+      - synchronize
+
+# Cancel the job if new commits are pushed
+# https://stackoverflow.com/questions/66335225/how-to-cancel-previous-runs-in-the-pr-when-you-push-new-commitsupdate-the-curre
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: flying-sheep/check@v1
+        with:
+          success: ${{ github.event_name == 'push' || contains(github.event.pull_request.labels.*.name, 'run-gpu-ci') }}
+  test:
+    name: GPU Tests
+    needs: check
+    runs-on: "cirun-aws-gpu--${{ github.run_id }}"
+    timeout-minutes: 30
+
+    defaults:
+      run:
+        shell: bash -el {0}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Nvidia SMI sanity check
+        run: nvidia-smi
+
+      - uses: mamba-org/setup-micromamba@v2
+        with:
+          environment-file: ci/environment_alpha.yml
+          init-shell: >-
+            bash
+          cache-environment: true
+          post-cleanup: 'all'
+
+      - name: Install rapids-singlecell
+        run: >-
+          pip install -e .[test]
+          "scanpy @ git+https://github.com/scverse/scanpy.git"
+          "anndata @ git+https://github.com/scverse/anndata.git"
+
+      - name: Pip list
+        run: pip list
+
+      - name: Run test
+        run: pytest

ci/environment_alpha.yml

@@ -0,0 +1,11 @@
+name: rapids_singlecell_nightly
+channels:
+ - rapidsai-nightly
+ - nvidia
+ - conda-forge
+dependencies:
+ - rapids=25.06
+ - python=3.13
+ - cuda-version=12.8
+ - zarr=3.0.6
+ - cudnn

docs/release-notes/0.12.4.md

@@ -16,4 +16,4 @@
 
 ```{rubric} Misc
 ```
-* Update notebooks for `anndata>0.11.4` {pr} `356` {smaller}`S Dicks`
+* Update notebooks for `anndata>0.11.4` {pr}`356` {smaller}`S Dicks`

docs/release-notes/0.12.5.md

@@ -0,0 +1,15 @@
+### 0.12.5 {small}`the-future`
+
+```{rubric} Features
+```
+
+```{rubric} Performance
+```
+
+```{rubric} Bug fixes
+```
+* remove `random_state` from cuml's `PCA` and `TruncatedSVD` {pr}`368` {smaller}`S Dicks`
+
+```{rubric} Misc
+```
+* adds tests for RAPIDS upcoming release {pr}`368` {smaller}`S Dicks`

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "rapids_singlecell"
 description = "running single cell analysis on Nvidia GPUs"
-requires-python = ">=3.10, <3.13"
+requires-python = ">=3.10, <3.14"
 license = { file = "LICENSE" }
 authors = [ { name = "Severin Dicks" } ]
 readme = { file = "README.md", content-type = "text/markdown" }

src/rapids_singlecell/preprocessing/_pca.py

@@ -197,7 +197,6 @@ def pca(
             pca_func = PCA(
                 n_components=n_comps,
                 svd_solver=svd_solver,
-                random_state=random_state,
                 output_type="numpy",
             )
             X_pca = pca_func.fit_transform(X)
@@ -207,7 +206,6 @@ def pca(
 
         pca_func = TruncatedSVD(
             n_components=n_comps,
-            random_state=random_state,
             algorithm=svd_solver,
             output_type="numpy",
         )

src/rapids_singlecell/preprocessing/_scrublet/pipeline.py

@@ -59,7 +59,7 @@ def truncated_svd(
     self._counts_obs_norm = self._counts_obs_norm.astype(cp.float32)
     self._counts_sim_norm = self._counts_sim_norm.astype(cp.float32)
     X_obs = _sparse_to_dense(self._counts_obs_norm)
-    svd = TruncatedSVD(n_components=n_prin_comps, random_state=random_state).fit(X_obs)
+    svd = TruncatedSVD(n_components=n_prin_comps).fit(X_obs)
     X_obs = svd.transform(X_obs)
     X_sim = svd.transform(_sparse_to_dense(self._counts_sim_norm))
     self.set_manifold(X_obs, X_sim)
@@ -80,7 +80,7 @@ def pca(
     self._counts_sim_norm = self._counts_sim_norm.astype(cp.float32)
     X_obs = _sparse_to_dense(self._counts_obs_norm)
 
-    pca = PCA(n_components=n_prin_comps, random_state=random_state).fit(X_obs)
+    pca = PCA(n_components=n_prin_comps).fit(X_obs)
     X_obs = pca.transform(X_obs)
     X_sim = pca.transform(_sparse_to_dense(self._counts_sim_norm))
     self.set_manifold(X_obs, X_sim)