[tune] BayesOpt - finish early when optimizer converges (#8808)

python/ray/tune/tests/test_convergence_gaussian_process.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+import ray
+from ray import tune
+from ray.tune.suggest.bayesopt import BayesOptSearch
+from ray.tune.suggest import ConcurrencyLimiter
+import unittest
+
+
+def loss(config, reporter):
+    x = config.get("x")
+    reporter(loss=x**2)  # A simple function to optimize
+
+
+class ConvergenceTest(unittest.TestCase):
+    """Test convergence in gaussian process."""
+
+    def test_convergence_gaussian_process(self):
+        np.random.seed(0)
+        ray.init(local_mode=True, num_cpus=1, num_gpus=1)
+
+        space = {
+            "x": (0, 20)  # This is the space of parameters to explore
+        }
+
+        resources_per_trial = {"cpu": 1, "gpu": 0}
+
+        # Following bayesian optimization
+        gp = BayesOptSearch(
+            space, metric="loss", mode="min", random_search_steps=10)
+        gp.repeat_float_precision = 5
+        gp = ConcurrencyLimiter(gp, 1)
+
+        # Execution of the BO.
+        analysis = tune.run(
+            loss,
+            # stop=EarlyStopping("loss", mode="min", patience=5),
+            search_alg=gp,
+            config={},
+            num_samples=100,  # Number of iterations
+            resources_per_trial=resources_per_trial,
+            raise_on_failed_trial=False,
+            fail_fast=True,
+            verbose=1)
+        assert len(analysis.trials) == 41
+
+        ray.shutdown()