cuml-cu12

import cuml import cupy as cp import numpy as np # For reproducibility np.random.seed(0) # Generate some random data on the CPU n_samples = 1000 n_features = 2 n_clusters = 3 X_host = np.random.rand(n_samples, n_features) * 10 # Simulate clusters for i in range(n_clusters): X_host[i*n_samples//n_clusters:(i+1)*n_samples//n_clusters] += i * 2 # Transfer data to GPU using CuPy X_gpu = cp.asarray(X_host, dtype=cp.float32) # Initialize and train KMeans on the GPU kmeans_cuml = cuml.cluster.KMeans(n_clusters=n_clusters, random_state=0) kmeans_cuml.fit(X_gpu) # Get cluster centers and labels (still on GPU) cluster_centers_gpu = kmeans_cuml.cluster_centers_ labels_gpu = kmeans_cuml.labels_ print("CuML KMeans fitted successfully.") print(f"Cluster Centers (on GPU):\n{cluster_centers_gpu}") print(f"First 10 Labels (on GPU): {labels_gpu[:10]}") # Transfer results back to CPU if needed cluster_centers_cpu = cluster_centers_gpu.get() labels_cpu = labels_gpu.get() print(f"Cluster Centers (on CPU):\n{cluster_centers_cpu}") print(f"First 10 Labels (on CPU): {labels_cpu[:10]}")