Fix documentation.

doc/tutorial.md

@@ -112,13 +112,6 @@ def add(a, b):
   return a + b
 ```
 
-The information passed to the `@ray.remote` decorator includes type information
-for the arguments and for the return values of the function. Because of the
-distinction that we make between *submitting a task* and *executing the task*,
-we require type information so that we can catch type errors when the remote
-function is called instead of catching them when the task is actually executed
-(which could be much later and could be on a different machine).
-
 ### Remote functions
 
 Whereas in regular Python, calling `add(1, 2)` would return `3`, in Ray, calling

examples/hyperopt/README.md

@@ -88,9 +88,7 @@ def train_cnn_and_compute_accuracy(hyperparameters, train_images, train_labels,
   return validation_accuracy
 ```
 
-The only difference is that we added the `@ray.remote` decorator specifying a
-little bit of type information (the input is a dictionary along with some numpy
-arrays, and the return value is a float).
+The only difference is that we added the `@ray.remote` decorator.
 
 Now a call to `train_cnn_and_compute_accuracy` does not execute the function. It
 submits the task to the scheduler and returns an object ID for the output

examples/lbfgs/README.md

@@ -102,9 +102,7 @@ def grad(theta, xs, ys):
   return grad
 ```
 
-The only difference is that we added the `@ray.remote` decorator specifying a
-little bit of type information (the inputs consist of numpy arrays, `loss`
-returns a float, and `grad` returns a numpy array).
+The only difference is that we added the `@ray.remote` decorator.
 
 Now, it is easy to speed up the computation of the full loss and the full
 gradient.