clean up imports (#230)

examples/hyperopt/driver.py

@@ -1,6 +1,5 @@
 import numpy as np
 import ray
-import ray.services as services
 import os
 
 import functions
@@ -11,7 +10,7 @@ epochs = 100
 
 worker_dir = os.path.dirname(os.path.abspath(__file__))
 worker_path = os.path.join(worker_dir, "worker.py")
-services.start_ray_local(num_workers=num_workers, worker_path=worker_path)
+ray.services.start_ray_local(num_workers=num_workers, worker_path=worker_path)
 
 best_params = None
 best_accuracy = 0

examples/hyperopt/functions.py

@@ -38,12 +38,12 @@ def train_cnn(params, epochs):
       sess.run(train_step, feed_dict={x: batch[0], y: batch[1], keep_prob: keep})
       if i % 100 == 0: # checks if accuracy is low enough to stop early every set number of epochs
         train_ac = accuracy.eval(feed_dict={x: batch[0], y: batch[1], keep_prob: 1.0})
-        if train_ac < 0.25: # Accuracy threshold is on a application to application basis. 
+        if train_ac < 0.25: # Accuracy threshold is on a application to application basis.
           totalacc = accuracy.eval(feed_dict={x: mnist.validation.images, y: mnist.validation.labels, keep_prob: 1.0})
           return totalacc
     totalacc = accuracy.eval(feed_dict={x: mnist.validation.images, y: mnist.validation.labels, keep_prob: 1.0})
-  return totalacc
-  
+  return totalacc.astype("float64")
+
 def cnn_setup(x, y, keep_prob, lr, stddev):
   first_hidden = 32
   second_hidden = 64

examples/hyperopt/worker.py

@@ -1,6 +1,5 @@
 import argparse
 import ray
-import ray.worker as worker
 
 import functions
 
@@ -13,4 +12,4 @@ if __name__ == "__main__":
   args = parser.parse_args()
   ray.connect(args.scheduler_address, args.objstore_address, args.worker_address)
   ray.register_module(functions)
-  worker.main_loop()
+  ray.worker.main_loop()

examples/imagenet/driver.py

@@ -3,20 +3,19 @@ import boto3
 import os
 import numpy as np
 import ray
-import ray.services as services
 import ray.datasets.imagenet as imagenet
 
 import functions
 
 parser = argparse.ArgumentParser(description="Parse information for data loading.")
-parser.add_argument("--s3-bucket", type=str, help="Name of the bucket that contains the image data.")
+parser.add_argument("--s3-bucket", type=str, required=True, help="Name of the bucket that contains the image data.")
 parser.add_argument("--key-prefix", default="ILSVRC2012_img_train/n015", type=str, help="Prefix for files to fetch.")
 parser.add_argument("--drop-ipython", default=False, type=bool, help="Drop into IPython at the end?")
 
 if __name__ == "__main__":
   args = parser.parse_args()
   worker_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "worker.py")
-  services.start_ray_local(num_workers=5, worker_path=worker_path)
+  ray.services.start_ray_local(num_workers=5, worker_path=worker_path)
 
   s3 = boto3.resource("s3")
   imagenet_bucket = s3.Bucket(args.s3_bucket)

examples/imagenet/worker.py

@@ -5,8 +5,6 @@ import numpy as np
 import ray.datasets.imagenet
 
 import ray
-import ray.services as services
-import ray.worker as worker
 import ray.array.remote as ra
 import ray.array.distributed as da
 
@@ -19,7 +17,7 @@ parser.add_argument("--worker-address", default="127.0.0.1:40001", type=str, hel
 
 if __name__ == "__main__":
   args = parser.parse_args()
-  worker.connect(args.scheduler_address, args.objstore_address, args.worker_address)
+  ray.worker.connect(args.scheduler_address, args.objstore_address, args.worker_address)
 
   ray.register_module(ray.datasets.imagenet)
   ray.register_module(functions)
@@ -30,4 +28,4 @@ if __name__ == "__main__":
   ray.register_module(da.random)
   ray.register_module(da.linalg)
 
-  worker.main_loop()
+  ray.worker.main_loop()

examples/lbfgs/worker.py

@@ -1,7 +1,6 @@
 import argparse
 
 import ray
-import ray.worker as worker
 
 import ray.array.remote as ra
 import ray.array.distributed as da
@@ -15,7 +14,7 @@ parser.add_argument("--worker-address", default="127.0.0.1:40001", type=str, hel
 
 if __name__ == "__main__":
   args = parser.parse_args()
-  worker.connect(args.scheduler_address, args.objstore_address, args.worker_address)
+  ray.worker.connect(args.scheduler_address, args.objstore_address, args.worker_address)
 
   ray.register_module(functions)
 
@@ -26,4 +25,4 @@ if __name__ == "__main__":
   ray.register_module(da.random)
   ray.register_module(da.linalg)
 
-  worker.main_loop()
+  ray.worker.main_loop()

examples/rl_pong/driver.py

@@ -5,14 +5,13 @@ import numpy as np
 import cPickle as pickle
 import gym
 import ray
-import ray.services as services
 import os
 
 import functions
 
 worker_dir = os.path.dirname(os.path.abspath(__file__))
 worker_path = os.path.join(worker_dir, "worker.py")
-services.start_ray_local(num_workers=10, worker_path=worker_path)
+ray.services.start_ray_local(num_workers=10, worker_path=worker_path)
 
 # hyperparameters
 H = 200 # number of hidden layer neurons

examples/rl_pong/worker.py

@@ -1,6 +1,5 @@
 import argparse
 import ray
-import ray.worker as worker
 import gym
 
 import functions
@@ -14,4 +13,4 @@ if __name__ == "__main__":
   args = parser.parse_args()
   ray.connect(args.scheduler_address, args.objstore_address, args.worker_address)
   ray.register_module(functions)
-  worker.main_loop()
+  ray.worker.main_loop()