Remove type information from remote decorator.

examples/alexnet/README.md

@@ -59,7 +59,7 @@ workers. At the core of our loading code is the remote function
 retrieves the appropriate object.
 
 ```python
-@ray.remote([str, str, List], [np.ndarray, List])
+@ray.remote(num_return_vals=2)
 def load_tarfile_from_s3(bucket, s3_key, size=[]):
   # Pull the object with the given key and bucket from S3, untar the contents,
   # and return it.
@@ -85,7 +85,7 @@ The other parallel component of this application is the training procedure. This
 is built on top of the remote function `compute_grad`.
 
 ```python
-@ray.remote([np.ndarray, np.ndarray, np.ndarray, List], [List])
+@ray.remote()
 def compute_grad(X, Y, mean, weights):
   # Load the weights into the network.
   # Subtract the mean and crop the images.

examples/alexnet/alexnet.py

@@ -39,7 +39,7 @@ def load_chunk(tarfile, size=None):
     filenames.append(filename)
   return np.concatenate(result), filenames
 
-@ray.remote([str, str, List], [np.ndarray, List])
+@ray.remote(num_return_vals=2)
 def load_tarfile_from_s3(bucket, s3_key, size=[]):
   """Load an imagenet .tar file.
 
@@ -231,7 +231,7 @@ def net_initialization():
 def net_reinitialization(net_vars):
   return net_vars
 
-@ray.remote([List], [int])
+@ray.remote()
 def num_images(batches):
   """Counts number of images in batches.
 
@@ -244,7 +244,7 @@ def num_images(batches):
   shape_ids = [ra.shape.remote(batch) for batch in batches]
   return sum([ray.get(shape_id)[0] for shape_id in shape_ids])
 
-@ray.remote([List], [np.ndarray])
+@ray.remote()
 def compute_mean_image(batches):
   """Computes the mean image given a list of batches of images.
 
@@ -261,7 +261,7 @@ def compute_mean_image(batches):
   n_images = num_images.remote(batches)
   return np.sum(sum_images, axis=0).astype("float64") / ray.get(n_images)
 
-@ray.remote([np.ndarray, np.ndarray, np.ndarray, np.ndarray], [np.ndarray, np.ndarray, np.ndarray, np.ndarray])
+@ray.remote(num_return_vals=4)
 def shuffle_arrays(first_images, first_labels, second_images, second_labels):
   """Shuffles the images and labels from two batches.
 
@@ -306,7 +306,7 @@ def shuffle_pair(first_batch, second_batch):
   images1, labels1, images2, labels2 = shuffle_arrays.remote(first_batch[0], first_batch[1], second_batch[0], second_batch[1])
   return (images1, labels1), (images2, labels2)
 
-@ray.remote([list, dict], [np.ndarray])
+@ray.remote()
 def filenames_to_labels(filenames, filename_label_dict):
   """Converts filename strings to integer labels.
 
@@ -381,7 +381,7 @@ def shuffle(batches):
     new_batches.append(permuted_batches[-1])
   return new_batches
 
-@ray.remote([np.ndarray, np.ndarray, np.ndarray, List], [List])
+@ray.remote()
 def compute_grad(X, Y, mean, weights):
   """Computes the gradient of the network.
 
@@ -406,7 +406,7 @@ def compute_grad(X, Y, mean, weights):
   # Compute the gradients.
   return sess.run([g for (g, v) in comp_grads], feed_dict={images: subset_X, y_true: subset_Y, dropout: 0.5})
 
-@ray.remote([np.ndarray, np.ndarray, List], [np.float32])
+@ray.remote()
 def compute_accuracy(X, Y, weights):
   """Returns the accuracy of the network
 

examples/hyperopt/README.md

@@ -82,7 +82,7 @@ complicated version of this remote function is defined in
 [hyperopt.py](hyperopt.py).
 
 ```python
-@ray.remote([dict, np.ndarray, np.ndarray, np.ndarray, np.ndarray], [float])
+@ray.remote()
 def train_cnn_and_compute_accuracy(hyperparameters, train_images, train_labels, validation_images, validation_labels):
   # Actual work omitted.
   return validation_accuracy

examples/hyperopt/hyperopt.py

@@ -51,7 +51,7 @@ def cnn_setup(x, y, keep_prob, lr, stddev):
 
 # Define a remote function that takes a set of hyperparameters as well as the
 # data, consructs and trains a network, and returns the validation accuracy.
-@ray.remote([dict, int, np.ndarray, np.ndarray, np.ndarray, np.ndarray], [float])
+@ray.remote()
 def train_cnn_and_compute_accuracy(params, steps, train_images, train_labels, validation_images, validation_labels):
   # Extract the hyperparameters from the params dictionary.
   learning_rate = params["learning_rate"]

examples/lbfgs/README.md

@@ -91,12 +91,12 @@ use remote functions to distribute the loading of the data.
 Now, lets turn `loss` and `grad` into remote functions.
 
 ```python
-@ray.remote([np.ndarray, np.ndarray, np.ndarray], [float])
+@ray.remote()
 def loss(theta, xs, ys):
   # compute the loss
   return loss
 
-@ray.remote([np.ndarray, np.ndarray, np.ndarray], [np.ndarray])
+@ray.remote()
 def grad(theta, xs, ys):
   # compute the gradient
   return grad

examples/lbfgs/driver.py

@@ -74,14 +74,14 @@ if __name__ == "__main__":
     sess.run([update_w, update_b], feed_dict={w_new: theta[:w_size].reshape(w_shape), b_new: theta[w_size:]})
 
   # Compute the loss on a batch of data.
-  @ray.remote([np.ndarray, np.ndarray, np.ndarray], [float])
+  @ray.remote()
   def loss(theta, xs, ys):
     sess, _, _, cross_entropy, _, x, y_, _, _ = ray.reusables.net_vars
     load_weights(theta)
     return float(sess.run(cross_entropy, feed_dict={x: xs, y_: ys}))
 
   # Compute the gradient of the loss on a batch of data.
-  @ray.remote([np.ndarray, np.ndarray, np.ndarray], [np.ndarray])
+  @ray.remote()
   def grad(theta, xs, ys):
     sess, _, _, _, cross_entropy_grads, x, y_, _, _ = ray.reusables.net_vars
     load_weights(theta)

examples/rl_pong/README.md

@@ -32,7 +32,7 @@ estimate of the gradient. Below is a simplified pseudocode version of this
 function.
 
 ```python
-@ray.remote([dict], [dict, float])
+@ray.remote(num_return_vals=2)
 def compute_gradient(model):
   # Retrieve the game environment.
   env = ray.reusables.env

examples/rl_pong/driver.py

@@ -72,7 +72,7 @@ def policy_backward(eph, epx, epdlogp, model):
   dW1 = np.dot(dh.T, epx)
   return {"W1": dW1, "W2": dW2}
 
-@ray.remote([dict], [dict, float])
+@ray.remote(num_return_vals=2)
 def compute_gradient(model):
   env = ray.reusables.env
   observation = env.reset()

examples/trpo/README.md

@@ -79,7 +79,7 @@ we use reusable variables to store the gym environment and the neural network po
 then used in the remote `do_rollout` function to do a remote rollout:
 
 ```python
-@ray.remote([np.ndarray, int, int], [dict])
+@ray.remote()
 def do_rollout(policy, timestep_limit, seed):
   # Retrieve the game environment.
   env = ray.reusables.env