def test_explicit_init(): import nums import nums.core.application_manager as am nums.init() assert am.is_initialized() am.destroy()
import nums import nums.numpy as nps from nums.models.glms import LogisticRegression nums.init() # Make dataset. X1 = nps.random.randn(500, 1) + 5.0 y1 = nps.zeros(shape=(500, ), dtype=bool) X2 = nps.random.randn(500, 1) + 10.0 y2 = nps.ones(shape=(500, ), dtype=bool) X = nps.concatenate([X1, X2], axis=0) y = nps.concatenate([y1, y2], axis=0) # Train Logistic Regression Model. model = LogisticRegression(solver="newton", tol=1e-8, max_iter=1) model.fit(X, y) y_pred = model.predict(X) print("accuracy", (nps.sum(y == y_pred) / X.shape[0]).get())
import ray import nums import nums.numpy as nps # Initialize ray and connect it to the cluster. ray.init(address="auto") # Initialize nums with the cluster shape. Here we set it to use all the nodes in the ray cluster. nums.init(cluster_shape=(len(ray.nodes()), 1)) def main(): X = nps.random.rand(10**4) Y = nps.random.rand(10**4) Z = nps.add(X, Y) print("X + Y = ", Z.get()) if __name__ == "__main__": main()