[tune] Dragonfly Optimizer (#5955)

* Add sample example * Copy relevant lines of ask from inherited Optimizer * Ignore strategy * Additional changes * Add DragonflySearch for tune connector for Dragonfly * Add example and fix small errors * lint * Remove skopt references * Update example based off of Dragonfly changes * Edit example for final Dragonfly edits * Formatting and documentation edits * Add documentation and add to test pipeline * Address PR comments * Fix Jenkins test * Adjust Dragonfly to PR#7366 * Lint * fix_tests Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
2026-07-16 11:21:10 +08:00 · 2020-03-10 08:40:36 -07:00
parent fa785a2ad2
commit 89ec4adb72
7 changed files with 274 additions and 12 deletions
@@ -0,0 +1,83 @@
+"""This test checks that Dragonfly is functional.
+
+It also checks that it is usable with a separate scheduler.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import ray
+from ray.tune import run
+from ray.tune.schedulers import AsyncHyperBandScheduler
+from ray.tune.suggest.dragonfly import DragonflySearch
+
+
+def objective(config, reporter):
+    import numpy as np
+    import time
+    time.sleep(0.2)
+    for i in range(config["iterations"]):
+        vol1 = config["point"][0]  # LiNO3
+        vol2 = config["point"][1]  # Li2SO4
+        vol3 = config["point"][2]  # NaClO4
+        vol4 = 10 - (vol1 + vol2 + vol3)  # Water
+        # Synthetic functions
+        conductivity = vol1 + 0.1 * (vol2 + vol3)**2 + 2.3 * vol4 * (vol1**1.5)
+        # Add Gaussian noise to simulate experimental noise
+        conductivity += np.random.normal() * 0.01
+        reporter(timesteps_total=i, objective=conductivity)
+        time.sleep(0.02)
+
+
+if __name__ == "__main__":
+    import argparse
+    from dragonfly.opt.gp_bandit import EuclideanGPBandit
+    from dragonfly.exd.experiment_caller import EuclideanFunctionCaller
+    from dragonfly import load_config
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    args, _ = parser.parse_known_args()
+    ray.init()
+
+    config = {
+        "num_samples": 10 if args.smoke_test else 50,
+        "config": {
+            "iterations": 100,
+        },
+        "stop": {
+            "timesteps_total": 100
+        },
+    }
+
+    domain_vars = [{
+        "name": "LiNO3_vol",
+        "type": "float",
+        "min": 0,
+        "max": 7
+    }, {
+        "name": "Li2SO4_vol",
+        "type": "float",
+        "min": 0,
+        "max": 7
+    }, {
+        "name": "NaClO4_vol",
+        "type": "float",
+        "min": 0,
+        "max": 7
+    }]
+
+    domain_config = load_config({"domain": domain_vars})
+
+    func_caller = EuclideanFunctionCaller(
+        None, domain_config.domain.list_of_domains[0])
+    optimizer = EuclideanGPBandit(func_caller, ask_tell_mode=True)
+    algo = DragonflySearch(
+        optimizer, max_concurrent=4, metric="objective", mode="max")
+    scheduler = AsyncHyperBandScheduler(metric="objective", mode="max")
+    run(objective,
+        name="dragonfly_search",
+        search_alg=algo,
+        scheduler=scheduler,
+        **config)