Updates required by recent changes in ray (#462)

* Resolve ray.tune deprecation warnings

* Resolve ray-project/ray#38202

* Change ray version requirements

* Fix tests

examples/examples_requirements.txt

@@ -1,7 +1,7 @@
 astra-toolbox
 colour_demosaicing
 xdesign>=0.5.5
-ray[tune]>=2.0.0
+ray[tune,train]>=2.5.0
 hyperopt
 bm3d>=4.0.0
 bm4d>=4.2.2

scico/ray/tune.py

@@ -18,6 +18,8 @@
 
 try:
     import ray.tune
+
+    os.environ["RAY_AIR_NEW_OUTPUT"] = "0"
 except ImportError:
     raise ImportError("Could not import ray.tune; please install it.")
 import ray.air
@@ -75,7 +77,7 @@ def run(
     config: Optional[Dict[str, Any]] = None,
     hyperopt: bool = True,
     verbose: bool = True,
-    local_dir: Optional[str] = None,
+    storage_path: Optional[str] = None,
 ) -> ray.tune.ExperimentAnalysis:
     """Simplified wrapper for `ray.tune.run`_.
 
@@ -109,7 +111,7 @@ def run(
             running, and terminated trials are indicated by "P:", "R:",
             and "T:" respectively, followed by the current best metric
             value and the parameters at which it was reported.
-        local_dir: Directory in which to save tuning results. Defaults to
+        storage_path: Directory in which to save tuning results. Defaults to
             a subdirectory "<username>/ray_results" within the path returned by
             `tempfile.gettempdir()`, corresponding e.g. to
             "/tmp/<username>/ray_results" under Linux.
@@ -136,12 +138,12 @@ def run(
         name = run_or_experiment.__name__
     name += "_" + datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
 
-    if local_dir is None:
+    if storage_path is None:
         try:
             user = getpass.getuser()
         except Exception:  # pragma: no cover
             user = "NOUSER"
-        local_dir = os.path.join(tempfile.gettempdir(), user, "ray_results")
+        storage_path = os.path.join(tempfile.gettempdir(), user, "ray_results")
 
     # Record original logger.info
     logger_info = ray.tune.tune.logger.info
@@ -160,7 +162,7 @@ def logger_info_filter(msg, *args, **kwargs):
         name=name,
         time_budget_s=time_budget_s,
         num_samples=num_samples,
-        local_dir=local_dir,
+        storage_path=storage_path,
         resources_per_trial=resources_per_trial,
         max_concurrent_trials=max_concurrent_trials,
         reuse_actors=True,
@@ -193,7 +195,7 @@ def __init__(
         reuse_actors: bool = True,
         hyperopt: bool = True,
         verbose: bool = True,
-        local_dir: Optional[str] = None,
+        storage_path: Optional[str] = None,
         **kwargs,
     ):
         """
@@ -226,7 +228,7 @@ def __init__(
               running, and terminated trials are indicated by "P:", "R:",
               and "T:" respectively, followed by the current best metric
               value and the parameters at which it was reported.
-           local_dir: Directory in which to save tuning results. Defaults
+           storage_path: Directory in which to save tuning results. Defaults
               to a subdirectory "<username>/ray_results" within the path
               returned by `tempfile.gettempdir()`, corresponding e.g. to
               "/tmp/<username>/ray_results" under Linux.
@@ -263,15 +265,15 @@ def __init__(
                 setattr(tune_config, k, v)
 
         name = trainable.__name__ + "_" + datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-        if local_dir is None:
+        if storage_path is None:
             try:
                 user = getpass.getuser()
             except Exception:  # pragma: no cover
                 user = "NOUSER"
-            local_dir = os.path.join(tempfile.gettempdir(), user, "ray_results")
+            storage_path = os.path.join(tempfile.gettempdir(), user, "ray_results")
 
         run_config = kwargs.pop("run_config", None)
-        run_config_kwargs = {"name": name, "local_dir": local_dir, "verbose": 0}
+        run_config_kwargs = {"name": name, "storage_path": storage_path, "verbose": 0}
         if verbose:
             run_config_kwargs.update({"verbose": 1, "progress_reporter": _CustomReporter()})
         if num_iterations is not None or time_budget is not None:

scico/test/test_ray_tune.py

@@ -7,7 +7,7 @@
 
 try:
     import ray
-    from scico.ray import train, tune
+    from scico.ray import report, tune
 
     ray.init(num_cpus=1)
 except ImportError as e:
@@ -18,7 +18,7 @@ def test_random_run():
     def eval_params(config):
         x, y = config["x"], config["y"]
         cost = x**2 + (y - 0.5) ** 2
-        train.report({"cost": cost})
+        report({"cost": cost})
 
     config = {"x": tune.uniform(-1, 1), "y": tune.uniform(-1, 1)}
     resources = {"gpu": 0, "cpu": 1}
@@ -32,7 +32,7 @@ def eval_params(config):
         resources_per_trial=resources,
         hyperopt=False,
         verbose=False,
-        local_dir=os.path.join(tempfile.gettempdir(), "ray_test"),
+        storage_path=os.path.join(tempfile.gettempdir(), "ray_test"),
     )
     best_config = analysis.get_best_config(metric="cost", mode="min")
     assert np.abs(best_config["x"]) < 0.25
@@ -43,7 +43,7 @@ def test_random_tune():
     def eval_params(config):
         x, y = config["x"], config["y"]
         cost = x**2 + (y - 0.5) ** 2
-        train.report({"cost": cost})
+        report({"cost": cost})
 
     config = {"x": tune.uniform(-1, 1), "y": tune.uniform(-1, 1)}
     resources = {"gpu": 0, "cpu": 1}
@@ -56,7 +56,7 @@ def eval_params(config):
         num_samples=100,
         hyperopt=False,
         verbose=False,
-        local_dir=os.path.join(tempfile.gettempdir(), "ray_test"),
+        storage_path=os.path.join(tempfile.gettempdir(), "ray_test"),
     )
     results = tuner.fit()
     best_config = results.get_best_result().config
@@ -68,7 +68,7 @@ def test_hyperopt_run():
     def eval_params(config):
         x, y = config["x"], config["y"]
         cost = x**2 + (y - 0.5) ** 2
-        train.report({"cost": cost})
+        report({"cost": cost})
 
     config = {"x": tune.uniform(-1, 1), "y": tune.uniform(-1, 1)}
     resources = {"gpu": 0, "cpu": 1}
@@ -91,7 +91,7 @@ def test_hyperopt_tune():
     def eval_params(config):
         x, y = config["x"], config["y"]
         cost = x**2 + (y - 0.5) ** 2
-        train.report({"cost": cost})
+        report({"cost": cost})
 
     config = {"x": tune.uniform(-1, 1), "y": tune.uniform(-1, 1)}
     resources = {"gpu": 0, "cpu": 1}
@@ -115,7 +115,7 @@ def test_hyperopt_tune_alt_init():
     def eval_params(config):
         x, y = config["x"], config["y"]
         cost = x**2 + (y - 0.5) ** 2
-        train.report({"cost": cost})
+        report({"cost": cost})
 
     config = {"x": tune.uniform(-1, 1), "y": tune.uniform(-1, 1)}
     tuner = tune.Tuner(