[tune] tune.track -> tune.report (#8388)

python/ray/tune/tests/example.py

@@ -23,7 +23,7 @@ def train_mnist(config):
     for i in range(10):
         train(model, optimizer, train_loader)
         acc = test(model, test_loader)
-        tune.track.log(mean_accuracy=acc)
+        tune.report(mean_accuracy=acc)
 
 
 analysis = tune.run(

python/ray/tune/tests/test_api.py

@@ -572,7 +572,7 @@ class TrainableFunctionApiTest(unittest.TestCase):
 
     def testReportInfinity(self):
         def train(config, reporter):
-            for i in range(100):
+            for _ in range(100):
                 reporter(mean_accuracy=float("inf"))
 
         register_trainable("f1", train)
@@ -606,8 +606,8 @@ class TrainableFunctionApiTest(unittest.TestCase):
         self.assertEqual(trial.last_result.get("trial_id"), trial.trial_id)
 
         def track_train(config):
-            tune.track.log(
-                name=tune.track.trial_name(), trial_id=tune.track.trial_id())
+            tune.report(
+                name=tune.get_trial_name(), trial_id=tune.get_trial_id())
 
         analysis = tune.run(track_train, stop={TRAINING_ITERATION: 1})
         trial = analysis.trials[0]

python/ray/tune/tests/test_track.py

@@ -1,11 +1,9 @@
-import os
 import pandas as pd
 import unittest
 
 import ray
 from ray import tune
-from ray.tune import track
-from ray.tune.result import EXPR_PARAM_FILE, EXPR_RESULT_FILE
+from ray.tune import session
 
 
 def _check_json_val(fname, key, val):
@@ -16,46 +14,24 @@ def _check_json_val(fname, key, val):
 
 class TrackApiTest(unittest.TestCase):
     def tearDown(self):
-        track.shutdown()
+        session.shutdown()
         ray.shutdown()
 
     def testSessionInitShutdown(self):
-        self.assertTrue(track._session is None)
+        self.assertTrue(session._session is None)
 
         # Checks that the singleton _session is created/destroyed
-        # by track.init() and track.shutdown()
+        # by session.init() and session.shutdown()
         for _ in range(2):
             # do it twice to see that we can reopen the session
-            track.init(trial_name="test_init")
-            self.assertTrue(track._session is not None)
-            track.shutdown()
-            self.assertTrue(track._session is None)
+            session.init(reporter=None)
+            self.assertTrue(session._session is not None)
+            session.shutdown()
+            self.assertTrue(session._session is None)
 
-    def testLogCreation(self):
-        """Checks that track.init() starts logger and creates log files."""
-        track.init(trial_name="test_init")
-        session = track.get_session()
-        self.assertTrue(session is not None)
-
-        self.assertTrue(os.path.isdir(session.logdir))
-
-        params_path = os.path.join(session.logdir, EXPR_PARAM_FILE)
-        result_path = os.path.join(session.logdir, EXPR_RESULT_FILE)
-
-        self.assertTrue(os.path.exists(params_path))
-        self.assertTrue(os.path.exists(result_path))
-        self.assertTrue(session.logdir == track.trial_dir())
-
-    def testMetric(self):
-        track.init(trial_name="test_log")
-        session = track.get_session()
-        for i in range(5):
-            track.log(test=i)
-        result_path = os.path.join(session.logdir, EXPR_RESULT_FILE)
-        self.assertTrue(_check_json_val(result_path, "test", i))
-
-    def testRayOutput(self):
-        """Checks that local and remote log format are the same."""
+    def testSoftDeprecation(self):
+        """Checks that tune.track.log code does not break."""
+        from ray.tune import track
         ray.init()
 
         def testme(config):
@@ -67,18 +43,6 @@ class TrackApiTest(unittest.TestCase):
         self.assertTrue(trial_res["hi"], "test")
         self.assertTrue(trial_res["training_iteration"], 5)
 
-    def testLocalMetrics(self):
-        """Checks that metric state is updated correctly."""
-        track.init(trial_name="test_logs")
-        session = track.get_session()
-        self.assertEqual(set(session.trial_config.keys()), {"trial_id"})
-
-        result_path = os.path.join(session.logdir, EXPR_RESULT_FILE)
-        track.log(test=1)
-        self.assertTrue(_check_json_val(result_path, "test", 1))
-        track.log(iteration=1, test=2)
-        self.assertTrue(_check_json_val(result_path, "test", 2))
-
 
 if __name__ == "__main__":
     import pytest

python/ray/tune/tests/tutorial.py

@@ -9,7 +9,6 @@ import torch.optim as optim
 from torchvision import datasets
 
 from ray import tune
-from ray.tune import track
 from ray.tune.schedulers import ASHAScheduler
 from ray.tune.examples.mnist_pytorch import get_data_loaders, ConvNet, train, test
 # __tutorial_imports_end__
@@ -26,7 +25,7 @@ def train_mnist(config):
     for i in range(10):
         train(model, optimizer, train_loader)
         acc = test(model, test_loader)
-        track.log(mean_accuracy=acc)
+        tune.report(mean_accuracy=acc)
         if i % 5 == 0:
             # This saves the model to the trial directory
             torch.save(model, "./model.pth")