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name: cuml description: >- Use when writing, running, debugging or upgrading Python code that uses NVIDIA cuML (GPU machine learning), cuml.accel (zero-code-change GPU acceleration of scikit-learn, umap-learn and hdbscan), or GPU UMAP/HDBSCAN/RandomForest/KMeans/SVC, including through libraries that wrap cuML (BERTopic, rapids-singlecell). Covers APIs removed or renamed in 2025-2026 releases, verifying code really ran on the GPU, large-data UMAP/HDBSCAN memory recipes, and exporting trained models. Not for cuDF-only dataframe work, PyTorch or XGBoost.

cuML

cuML releases every two months and removes deprecated APIs, so your training data is likely to contain APIs that no longer exist. Look things up in the installed cuML instead of recalling them.

Ground facts

• Install from pypi.org: pip install cuml-cu13 (CUDA 13 driver, >= 580) or cuml-cu12. No extra index URL is needed. pip install cuml installs an unrelated placeholder.
• Check the RAPIDS installation guide for supported GPUs, operating systems, Python versions, CUDA/driver requirements, and install commands. Linux only; on Windows use WSL2.
• Google Colab GPU runtimes ship cuML preinstalled: check pip list before installing anything there.
• cuml.accel runs unmodified scikit-learn / umap-learn / hdbscan code on the GPU and falls back to the CPU for anything unsupported. It accelerates RandomForest, LogisticRegression, Ridge, KMeans, DBSCAN, PCA, SVC, KNN, UMAP, HDBSCAN, scalers, encoders and more.
• Enable it with python -m cuml.accel script.py, %load_ext cuml.accel (first notebook cell), CUML_ACCEL_ENABLED=1, or cuml.accel.install() before importing sklearn.

Step 1: look it up in the installed cuML

• Check the versions and answer for them: python -c "import cuml, sklearn; print(cuml.__version__, sklearn.__version__)".
• Before using a cuML class, function or argument, confirm it exists and read its documentation: python -c "import inspect, cuml; print(inspect.signature(cuml.svm.SVC))", help(cuml.manifold.UMAP). Docstrings list valid values, for example the accepted build_kwds keys and output_type values.
• Run the code and read the errors: removed arguments raise TypeError, removed modules ModuleNotFoundError, invalid values usually list the valid ones.
• Treat deprecated APIs as removed: do not recommend anything that emits a FutureWarning or is marked deprecated in its docstring, even if it still works in the installed version. Deprecation warnings only appear when the code runs, so run the code you suggest (on small data if needed) with python -W always::FutureWarning and read the warnings; they name the replacement.
• For cuml.accel, see below for how to find out what runs on the GPU.

Step 2: check code for removed APIs

Run python scripts/check_removed_apis.py <files or dirs> (path relative to this skill) on the user's code and on code you write, before presenting it. It reports removed APIs whose replacement the installed cuML does not reveal, with the replacement, for example cuml.fil (use nvForest), SVC(probability=True) (use CalibratedClassifierCV) and renamed UMAP/HDBSCAN build_kwds keys that are silently ignored.

The checker only knows about these few APIs. An empty result does not mean the code is current, and it says nothing about whether cuml.accel runs it on the GPU; still do Step 1 and check cuml.accel fallbacks as described below.

Behavior that changed without an API change:

• RandomForest is reproducible with random_state alone (since 25.10). Do not set n_streams=1 "for determinism"; that advice is outdated and only slows training.
• KMeans.transform returns Euclidean, not squared, distances (since 26.10).

cuml.accel

• Activate before sklearn is imported. In a notebook that already imported sklearn, restart the kernel and make %load_ext cuml.accel the first cell.
• With cudf.pandas: python -m cudf.pandas -m cuml.accel script.py, or %load_ext cudf.pandas then %load_ext cuml.accel.
• Prove where code ran instead of assuming: python -m cuml.accel -v script.py logs "ran on GPU" / "falling back to CPU: <reason>"; --profile (or %%cuml.accel.profile, or with cuml.accel.profile():) prints a per-call GPU/CPU table. cuml.accel.is_proxy(est) only says the estimator is wrapped, not that it ran on the GPU.
• Unsupported parameters and inputs fall back to the CPU silently. Do not recall fallback lists: run with -v or --profile and read the reasons, or, without running, read the documentation for the installed release from GitHub: https://raw.githubusercontent.com/NVIDIA/cuml/v<cuml.__version__>/docs/source/cuml-accel/compatibility.rst (for example v26.08.00; for 26.06 and earlier the file is limitations.rst; for nightly builds such as 26.12.00a5 use main instead of the tag).
• Single GPU only. To use several GPUs, run separate processes with CUDA_VISIBLE_DEVICES.
• Models trained under accel pickle as plain sklearn/umap/hdbscan objects and load without cuML. The pickle bytes still contain the string cuml (a reconstructor), so do not use "no cuml in the bytes" as a check; load it in a CPU-only environment instead.
• Results are comparable in quality, not numerically identical, to the CPU libraries. See references/parity-and-numerics.md before treating a difference as a bug.

Large data (UMAP, HDBSCAN, KMeans)

• UMAP build_algo="auto" already uses NN-Descent above 50,000 rows (unless random_state is set, which forces brute force). HDBSCAN defaults to build_algo="brute_force".
• Out of memory on the kNN build: batch it with build_algo="nn_descent", build_kwds={"knn_n_clusters": 4, "knn_overlap_factor": 2} (increase knn_n_clusters to 8, 16, ... to use less memory). Unknown keys are ignored without a warning, so spell them exactly.
• Oversubscribe GPU memory with managed memory: import rmm; rmm.reinitialize(managed_memory=True) before creating estimators. cuml.accel enables managed memory by default (not on WSL2).
• KMeans on host data larger than the GPU: KMeans(device_buffer_samples=...) streams host batches (26.08+).
• HDBSCAN soft clustering: fit with prediction_data=True, then use cuml.cluster.hdbscan.all_points_membership_vectors, membership_vector and approximate_predict. There is no HDBSCAN.predict and no .prediction submodule.

Exporting models

| Have | Want | Use | |---|---|---| | cuML estimator | scikit-learn object for a CPU-only machine | est.as_sklearn() | | scikit-learn estimator | cuML estimator | cuml.<Estimator>.from_sklearn(skl) | | cuML RandomForest | Treelite / fast GPU or CPU inference | rf.as_treelite(), rf.as_nvforest() | | sklearn / XGBoost / LightGBM forest | fast inference | nvforest.load_from_sklearn(model), nvforest.load_model(path) | | accel-trained model | ONNX | skl2onnx on the proxy (not DBSCAN, TSNE, UMAP, HDBSCAN, NearestNeighbors) |

Outputs

Outputs mirror the input container: NumPy in, NumPy out; cuDF in, cuDF out. With cuDF or dask_cudf input, fitted attributes such as cluster_centers_ are cuDF objects, so call .to_pandas() / .to_numpy() before handing them to pandas or matplotlib. Force a type with output_type="cupy" on the estimator, cuml.set_global_output_type("cupy"), or with cuml.using_output_type("cupy"): (global and context settings take precedence). Non-float input is converted to float32 by default.