From 5cf1d60cb27270dc742f012971cc815c30eff81f Mon Sep 17 00:00:00 2001 From: Robert Nishihara Date: Tue, 30 Aug 2016 17:20:00 -0700 Subject: [PATCH] Fix documentation. --- doc/tutorial.md | 7 ------- examples/hyperopt/README.md | 4 +--- examples/lbfgs/README.md | 4 +--- 3 files changed, 2 insertions(+), 13 deletions(-) diff --git a/doc/tutorial.md b/doc/tutorial.md index f031f0e50..0d5f07e0a 100644 --- a/doc/tutorial.md +++ b/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 diff --git a/examples/hyperopt/README.md b/examples/hyperopt/README.md index cf3ea399d..67dd2847d 100644 --- a/examples/hyperopt/README.md +++ b/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 diff --git a/examples/lbfgs/README.md b/examples/lbfgs/README.md index addfc5dbc..7703cf025 100644 --- a/examples/lbfgs/README.md +++ b/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.