[tune][minor] formatting examples, fix travis (ray-project#5869)

* formatting * formatting
jbn · Oct 9, 2019 · 1181924 · 1181924
1 parent a851d7e
commit 1181924
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Showing 6 changed files with 70 additions and 80 deletions.
diff --git a/doc/examples/plot_hyperparameter.py b/doc/examples/plot_hyperparameter.py
@@ -70,6 +70,7 @@ def get_data_loaders(batch_size):
         shuffle=True)
     return train_loader, test_loader
 
+
 #######################################################################
 # Setup: Defining the Neural Network
 # ----------------------------------
@@ -130,6 +131,7 @@ def test(model, test_loader, device=torch.device("cpu")):
 
     return correct / total
 
+
 #######################################################################
 # Evaluating the Hyperparameters
 # -------------------------------
@@ -141,6 +143,7 @@ def test(model, test_loader, device=torch.device("cpu")):
 #
 # The ``@ray.remote`` decorator defines a remote process.
 
+
 @ray.remote
 def evaluate_hyperparameters(config):
     model = ConvNet()
@@ -152,14 +155,14 @@ def evaluate_hyperparameters(config):
     train(model, optimizer, train_loader)
     return test(model, test_loader)
 
+
 #######################################################################
 # Synchronous Evaluation of Randomly Generated Hyperparameters
 # ------------------------------------------------------------
 #
 # We will create multiple sets of random hyperparameters for our neural
 # network that will be evaluated in parallel.
 
-
 # Keep track of the best hyperparameters and the best accuracy.
 best_hyperparameters = None
 best_accuracy = 0

diff --git a/python/ray/tune/examples/async_hyperband_example.py b/python/ray/tune/examples/async_hyperband_example.py
@@ -70,18 +70,13 @@ def _restore(self, checkpoint_path):
     run(MyTrainableClass,
         name="asynchyperband_test",
         scheduler=ahb,
-        **{
-            "stop": {
-                "training_iteration": 1 if args.smoke_test else 99999
-            },
-            "num_samples": 20,
-            "resources_per_trial": {
-                "cpu": 1,
-                "gpu": 0
-            },
-            "config": {
-                "width": sample_from(
-                    lambda spec: 10 + int(90 * random.random())),
-                "height": sample_from(lambda spec: int(100 * random.random())),
-            },
+        stop={"training_iteration": 1 if args.smoke_test else 99999},
+        num_samples=20,
+        resources_per_trial={
+            "cpu": 1,
+            "gpu": 0
+        },
+        config={
+            "width": sample_from(lambda spec: 10 + int(90 * random.random())),
+            "height": sample_from(lambda spec: int(100 * random.random())),
         })
diff --git a/python/ray/tune/examples/mnist_pytorch_trainable.py b/python/ray/tune/examples/mnist_pytorch_trainable.py
@@ -69,23 +69,21 @@ def _restore(self, checkpoint_path):
     analysis = tune.run(
         TrainMNIST,
         scheduler=sched,
-        **{
-            "stop": {
-                "mean_accuracy": 0.95,
-                "training_iteration": 3 if args.smoke_test else 20,
-            },
-            "resources_per_trial": {
-                "cpu": 3,
-                "gpu": int(args.use_gpu)
-            },
-            "num_samples": 1 if args.smoke_test else 20,
-            "checkpoint_at_end": True,
-            "checkpoint_freq": 3,
-            "config": {
-                "args": args,
-                "lr": tune.uniform(0.001, 0.1),
-                "momentum": tune.uniform(0.1, 0.9),
-            }
+        stop={
+            "mean_accuracy": 0.95,
+            "training_iteration": 3 if args.smoke_test else 20,
+        },
+        resources_per_trial={
+            "cpu": 3,
+            "gpu": int(args.use_gpu)
+        },
+        num_samples=1 if args.smoke_test else 20,
+        checkpoint_at_end=True,
+        checkpoint_freq=3,
+        config={
+            "args": args,
+            "lr": tune.uniform(0.001, 0.1),
+            "momentum": tune.uniform(0.1, 0.9),
         })
 
     print("Best config is:", analysis.get_best_config(metric="mean_accuracy"))
diff --git a/python/ray/tune/examples/pbt_example.py b/python/ray/tune/examples/pbt_example.py
@@ -112,15 +112,13 @@ def reset_config(self, new_config):
         scheduler=pbt,
         reuse_actors=True,
         verbose=False,
-        **{
-            "stop": {
-                "training_iteration": 2000,
-            },
-            "num_samples": 4,
-            "config": {
-                "lr": 0.0001,
-                # note: this parameter is perturbed but has no effect on
-                # the model training in this example
-                "some_other_factor": 1,
-            },
+        stop={
+            "training_iteration": 2000,
+        },
+        num_samples=4,
+        config={
+            "lr": 0.0001,
+            # note: this parameter is perturbed but has no effect on
+            # the model training in this example
+            "some_other_factor": 1,
         })
diff --git a/python/ray/tune/examples/pbt_ppo_example.py b/python/ray/tune/examples/pbt_ppo_example.py
@@ -53,26 +53,24 @@ def explore(config):
         "PPO",
         name="pbt_humanoid_test",
         scheduler=pbt,
-        **{
-            "num_samples": 8,
-            "config": {
-                "env": "Humanoid-v1",
-                "kl_coeff": 1.0,
-                "num_workers": 8,
-                "num_gpus": 1,
-                "model": {
-                    "free_log_std": True
-                },
-                # These params are tuned from a fixed starting value.
-                "lambda": 0.95,
-                "clip_param": 0.2,
-                "lr": 1e-4,
-                # These params start off randomly drawn from a set.
-                "num_sgd_iter": sample_from(
-                    lambda spec: random.choice([10, 20, 30])),
-                "sgd_minibatch_size": sample_from(
-                    lambda spec: random.choice([128, 512, 2048])),
-                "train_batch_size": sample_from(
-                    lambda spec: random.choice([10000, 20000, 40000]))
+        num_samples=8,
+        config={
+            "env": "Humanoid-v1",
+            "kl_coeff": 1.0,
+            "num_workers": 8,
+            "num_gpus": 1,
+            "model": {
+                "free_log_std": True
             },
+            # These params are tuned from a fixed starting value.
+            "lambda": 0.95,
+            "clip_param": 0.2,
+            "lr": 1e-4,
+            # These params start off randomly drawn from a set.
+            "num_sgd_iter": sample_from(
+                lambda spec: random.choice([10, 20, 30])),
+            "sgd_minibatch_size": sample_from(
+                lambda spec: random.choice([128, 512, 2048])),
+            "train_batch_size": sample_from(
+                lambda spec: random.choice([10000, 20000, 40000]))
         })
diff --git a/python/ray/tune/examples/tune_cifar10_gluon.py b/python/ray/tune/examples/tune_cifar10_gluon.py
@@ -206,20 +206,18 @@ def test():
         name=args.expname,
         verbose=2,
         scheduler=sched,
-        **{
-            "stop": {
-                "mean_accuracy": 0.98,
-                "training_iteration": 1 if args.smoke_test else args.epochs
-            },
-            "resources_per_trial": {
-                "cpu": int(args.num_workers),
-                "gpu": int(args.num_gpus)
-            },
-            "num_samples": 1 if args.smoke_test else args.num_samples,
-            "config": {
-                "lr": tune.sample_from(
-                    lambda spec: np.power(10.0, np.random.uniform(-4, -1))),
-                "momentum": tune.sample_from(
-                    lambda spec: np.random.uniform(0.85, 0.95)),
-            }
+        stop={
+            "mean_accuracy": 0.98,
+            "training_iteration": 1 if args.smoke_test else args.epochs
+        },
+        resources_per_trial={
+            "cpu": int(args.num_workers),
+            "gpu": int(args.num_gpus)
+        },
+        num_samples=1 if args.smoke_test else args.num_samples,
+        config={
+            "lr": tune.sample_from(
+                lambda spec: np.power(10.0, np.random.uniform(-4, -1))),
+            "momentum": tune.sample_from(
+                lambda spec: np.random.uniform(0.85, 0.95)),
         })