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 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.