[docs] Move all /latest links to /master (#11897)

* use master link

* remae

* revert non-ray

* more

* mre

.github/ISSUE_TEMPLATE/bug_report.md

@@ -19,4 +19,4 @@ Please provide a script that can be run to reproduce the issue. The script shoul
 If we cannot run your script, we cannot fix your issue.
 
 - [ ] I have verified my script runs in a clean environment and reproduces the issue.
-- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
+- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/master/installation.html).

README.rst

@@ -1,7 +1,7 @@
 .. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png
 
-.. image:: https://readthedocs.org/projects/ray/badge/?version=latest
-    :target: http://docs.ray.io/en/latest/?badge=latest
+.. image:: https://readthedocs.org/projects/ray/badge/?version=master
+    :target: http://docs.ray.io/en/master/?badge=master
 
 .. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue
     :target: https://forms.gle/9TSdDYUgxYs8SA9e8
@@ -15,7 +15,7 @@ Ray is packaged with the following libraries for accelerating machine learning w
 
 - `Tune`_: Scalable Hyperparameter Tuning
 - `RLlib`_: Scalable Reinforcement Learning
-- `RaySGD <https://docs.ray.io/en/latest/raysgd/raysgd.html>`__: Distributed Training Wrappers
+- `RaySGD <https://docs.ray.io/en/master/raysgd/raysgd.html>`__: Distributed Training Wrappers
 - `Ray Serve`_: Scalable and Programmable Serving
 
 There are also many `community integrations <https://docs.ray.io/en/master/ray-libraries.html>`_ with Ray, including `Dask`_, `MARS`_, `Modin`_, `Horovod`_, `Hugging Face`_, `Scikit-learn`_, and others. Check out the `full list of Ray distributed libraries here <https://docs.ray.io/en/master/ray-libraries.html>`_.
@@ -78,7 +78,7 @@ Ray programs can run on a single machine, and can also seamlessly scale to large
 
 ``ray submit [CLUSTER.YAML] example.py --start``
 
-Read more about `launching clusters <https://docs.ray.io/en/latest/cluster/index.html>`_.
+Read more about `launching clusters <https://docs.ray.io/en/master/cluster/index.html>`_.
 
 Tune Quick Start
 ----------------
@@ -140,10 +140,10 @@ If TensorBoard is installed, automatically visualize all trial results:
 
     tensorboard --logdir ~/ray_results
 
-.. _`Tune`: https://docs.ray.io/en/latest/tune.html
-.. _`Population Based Training (PBT)`: https://docs.ray.io/en/latest/tune-schedulers.html#population-based-training-pbt
-.. _`Vizier's Median Stopping Rule`: https://docs.ray.io/en/latest/tune-schedulers.html#median-stopping-rule
-.. _`HyperBand/ASHA`: https://docs.ray.io/en/latest/tune-schedulers.html#asynchronous-hyperband
+.. _`Tune`: https://docs.ray.io/en/master/tune.html
+.. _`Population Based Training (PBT)`: https://docs.ray.io/en/master/tune-schedulers.html#population-based-training-pbt
+.. _`Vizier's Median Stopping Rule`: https://docs.ray.io/en/master/tune-schedulers.html#median-stopping-rule
+.. _`HyperBand/ASHA`: https://docs.ray.io/en/master/tune-schedulers.html#asynchronous-hyperband
 
 RLlib Quick Start
 -----------------
@@ -189,7 +189,7 @@ RLlib Quick Start
             "num_workers": 4,
             "env_config": {"corridor_length": 5}})
 
-.. _`RLlib`: https://docs.ray.io/en/latest/rllib.html
+.. _`RLlib`: https://docs.ray.io/en/master/rllib.html
 
 
 Ray Serve Quick Start
@@ -264,7 +264,7 @@ This example runs serves a scikit-learn gradient boosting classifier.
     # }
 
 
-.. _`Ray Serve`: https://docs.ray.io/en/latest/serve/index.html
+.. _`Ray Serve`: https://docs.ray.io/en/master/serve/index.html
 
 More Information
 ----------------
@@ -282,7 +282,7 @@ More Information
 - `Ray HotOS paper`_
 - `Blog (old)`_
 
-.. _`Documentation`: http://docs.ray.io/en/latest/index.html
+.. _`Documentation`: http://docs.ray.io/en/master/index.html
 .. _`Tutorial`: https://github.com/ray-project/tutorial
 .. _`Blog (old)`: https://ray-project.github.io/
 .. _`Blog`: https://medium.com/distributed-computing-with-ray

dashboard/client/src/pages/dashboard/logical-view/ActorDetailsPane.tsx

@@ -13,7 +13,7 @@ import { sum } from "../../../common/util";
 import ActorStateRepr from "./ActorStateRepr";
 
 const memoryDebuggingDocLink =
-  "https://docs.ray.io/en/latest/memory-management.html#debugging-using-ray-memory";
+  "https://docs.ray.io/en/master/memory-management.html#debugging-using-ray-memory";
 
 type ActorDatum = {
   label: string;

dashboard/client/src/pages/dashboard/tune/Tune.tsx

@@ -143,7 +143,7 @@ class Tune extends React.Component<
             You can use this tab to monitor Tune jobs, their statuses,
             hyperparameters, and more. For more information, read the
             documentation{" "}
-            <a href="https://docs.ray.io/en/latest/ray-dashboard.html#tune">
+            <a href="https://docs.ray.io/en/master/ray-dashboard.html#tune">
               here
             </a>
             .

doc/examples/cython/ray-project/project.yaml

@@ -4,7 +4,7 @@ name: ray-example-cython
 
 description: "Example of how to use Cython with ray"
 tags: ["ray-example", "cython"]
-documentation: https://docs.ray.io/en/latest/advanced.html#cython-code-in-ray
+documentation: https://docs.ray.io/en/master/advanced.html#cython-code-in-ray
 
 cluster:
   config: ray-project/cluster.yaml

doc/examples/lbfgs/ray-project/project.yaml

@@ -4,7 +4,7 @@ name: ray-example-lbfgs
 
 description: "Parallelizing the L-BFGS algorithm in ray"
 tags: ["ray-example", "optimization", "lbfgs"]
-documentation: https://docs.ray.io/en/latest/auto_examples/plot_lbfgs.html
+documentation: https://docs.ray.io/en/master/auto_examples/plot_lbfgs.html
 
 cluster:
   config: ray-project/cluster.yaml

doc/examples/newsreader/ray-project/project.yaml

@@ -4,7 +4,7 @@ name: ray-example-newsreader
 
 description: "A simple news reader example that uses ray actors to serve requests"
 tags: ["ray-example", "flask", "rss", "newsreader"]
-documentation: https://docs.ray.io/en/latest/auto_examples/plot_newsreader.html
+documentation: https://docs.ray.io/en/master/auto_examples/plot_newsreader.html
 
 cluster:
   config: ray-project/cluster.yaml

doc/examples/overview.rst

@@ -90,7 +90,7 @@ Machine Learning Examples
 Reinforcement Learning Examples
 -------------------------------
 
-These are simple examples that show you how to leverage Ray Core. For Ray's production-grade reinforcement learning library, see `RLlib <http://docs.ray.io/en/latest/rllib.html>`__.
+These are simple examples that show you how to leverage Ray Core. For Ray's production-grade reinforcement learning library, see `RLlib <http://docs.ray.io/en/master/rllib.html>`__.
 
 .. raw:: html
 

doc/examples/plot_example-a3c.rst

@@ -13,7 +13,7 @@ View the `code for this example`_.
 
 .. note::
 
-    For an overview of Ray's reinforcement learning library, see `RLlib <http://docs.ray.io/en/latest/rllib.html>`__.
+    For an overview of Ray's reinforcement learning library, see `RLlib <http://docs.ray.io/en/master/rllib.html>`__.
 
 To run the application, first install **ray** and then some dependencies:
 

doc/examples/plot_hyperparameter.py

@@ -16,7 +16,7 @@ their results to be ready.
     hyperparameter tuning, use `Tune`_, a scalable hyperparameter
     tuning library built using Ray's Actor API.
 
-.. _`Tune`: https://docs.ray.io/en/latest/tune.html
+.. _`Tune`: https://docs.ray.io/en/master/tune.html
 
 Setup: Dependencies
 -------------------

doc/examples/plot_streaming.rst

@@ -87,7 +87,7 @@ the top 10 words in these articles together with their word count:
 Note that this examples uses `distributed actor handles`_, which are still
 considered experimental.
 
-.. _`distributed actor handles`: http://docs.ray.io/en/latest/actors.html
+.. _`distributed actor handles`: http://docs.ray.io/en/master/actors.html
 
 There is a ``Mapper`` actor, which has a method ``get_range`` used to retrieve
 word counts for words in a certain range:

doc/site/_posts/2017-05-17-announcing-ray.markdown

@@ -8,7 +8,7 @@ date: 2017-05-20 14:00:00
 This post announces Ray, a framework for efficiently running Python code on
 clusters and large multi-core machines. The project is open source.
 You can check out [the code](https://github.com/ray-project/ray) and
-[the documentation](http://docs.ray.io/en/latest/?badge=latest).
+[the documentation](http://docs.ray.io/en/master/?badge=latest).
 
 Many AI algorithms are computationally intensive and exhibit complex
 communication patterns. As a result, many researchers spend most of their

doc/site/_posts/2017-09-30-ray-0.2-release.markdown

@@ -134,12 +134,12 @@ state of the actor. We are working on improving the speed of recovery by
 enabling actor state to be restored from checkpoints. See [an overview of fault
 tolerance in Ray][4].
 
-[1]: http://docs.ray.io/en/latest/plasma-object-store.html
-[2]: http://docs.ray.io/en/latest/webui.html
-[3]: http://docs.ray.io/en/latest/rllib.html
-[4]: http://docs.ray.io/en/latest/fault-tolerance.html
+[1]: http://docs.ray.io/en/master/plasma-object-store.html
+[2]: http://docs.ray.io/en/master/webui.html
+[3]: http://docs.ray.io/en/master/rllib.html
+[4]: http://docs.ray.io/en/master/fault-tolerance.html
 [5]: https://github.com/apache/arrow
-[6]: http://docs.ray.io/en/latest/example-a3c.html
+[6]: http://docs.ray.io/en/master/example-a3c.html
 [7]: https://github.com/openai/baselines
 [8]: https://github.com/ray-project/ray/blob/b020e6bf1fb00d0745371d8674146d4a5b75d9f0/python/ray/rllib/test/tuned_examples.sh#L11
 [9]: https://arrow.apache.org/docs/python/ipc.html#arbitrary-object-serialization

doc/site/_posts/2017-10-15-fast-python-serialization-with-ray-and-arrow.markdown

@@ -271,7 +271,7 @@ for i in range(len(test_objects)):
     plot(*benchmark_object(test_objects[i]), titles[i], i)
 ```
 
-[1]: http://docs.ray.io/en/latest/index.html
+[1]: http://docs.ray.io/en/master/index.html
 [2]: https://arrow.apache.org/
 [3]: https://en.wikipedia.org/wiki/Serialization
 [4]: https://github.com/cloudpipe/cloudpickle/

doc/site/_posts/2017-11-30-ray-0.3-release.markdown

@@ -134,14 +134,14 @@ This feature is still considered experimental, but we've already found
 distributed actor handles useful for implementing [**parameter server**][10] and
 [**streaming MapReduce**][11] applications.
 
-[1]: http://docs.ray.io/en/latest/actors.html#passing-around-actor-handles-experimental
-[2]: http://docs.ray.io/en/latest/tune.html
-[3]: http://docs.ray.io/en/latest/rllib.html
+[1]: http://docs.ray.io/en/master/actors.html#passing-around-actor-handles-experimental
+[2]: http://docs.ray.io/en/master/tune.html
+[3]: http://docs.ray.io/en/master/rllib.html
 [4]: https://research.google.com/pubs/pub46180.html
 [5]: https://arxiv.org/abs/1603.06560
 [6]: https://www.tensorflow.org/get_started/summaries_and_tensorboard
 [7]: https://media.readthedocs.org/pdf/rllab/latest/rllab.pdf
 [8]: https://en.wikipedia.org/wiki/Parallel_coordinates
 [9]: https://github.com/ray-project/ray/tree/master/python/ray/tune
-[10]: http://docs.ray.io/en/latest/example-parameter-server.html
-[11]: http://docs.ray.io/en/latest/example-streaming.html
+[10]: http://docs.ray.io/en/master/example-parameter-server.html
+[11]: http://docs.ray.io/en/master/example-streaming.html

doc/site/_posts/2018-03-27-ray-0.4-release.markdown

@@ -78,10 +78,10 @@ Training][9].
 
 [1]: https://github.com/ray-project/ray
 [2]: https://rise.cs.berkeley.edu/blog/pandas-on-ray/
-[3]: http://docs.ray.io/en/latest/rllib.html
-[4]: http://docs.ray.io/en/latest/tune.html
+[3]: http://docs.ray.io/en/master/rllib.html
+[4]: http://docs.ray.io/en/master/tune.html
 [5]: https://rise.cs.berkeley.edu/blog/distributed-policy-optimizers-for-scalable-and-reproducible-deep-rl/
-[6]: http://docs.ray.io/en/latest/resources.html
+[6]: http://docs.ray.io/en/master/resources.html
 [7]: https://pandas.pydata.org/
 [8]: https://arxiv.org/abs/1803.00933
-[9]: http://docs.ray.io/en/latest/pbt.html
+[9]: http://docs.ray.io/en/master/pbt.html

doc/site/_posts/2018-07-06-ray-0.5-release.markdown

@@ -76,8 +76,8 @@ Ray now supports Java thanks to contributions from [Ant Financial][4]:
 
 
 [1]: https://github.com/ray-project/ray
-[2]: http://docs.ray.io/en/latest/rllib.html
-[3]: http://docs.ray.io/en/latest/tune.html
+[2]: http://docs.ray.io/en/master/rllib.html
+[3]: http://docs.ray.io/en/master/tune.html
 [4]: https://www.antfin.com/
 [5]: https://github.com/modin-project/modin
-[6]: http://docs.ray.io/en/latest/autoscaling.html
+[6]: http://docs.ray.io/en/master/autoscaling.html

doc/site/_posts/2018-07-15-parameter-server-in-fifteen-lines.markdown

@@ -321,12 +321,12 @@ Questions should be directed to *ray-dev@googlegroups.com*.
 
 
 [1]: https://github.com/ray-project/ray
-[2]: http://docs.ray.io/en/latest/resources.html
+[2]: http://docs.ray.io/en/master/resources.html
 [3]: http://www.sysml.cc/doc/206.pdf
-[4]: http://docs.ray.io/en/latest/rllib.html
-[5]: http://docs.ray.io/en/latest/tune.html
-[6]: http://docs.ray.io/en/latest
-[7]: http://docs.ray.io/en/latest/api.html
+[4]: http://docs.ray.io/en/master/rllib.html
+[5]: http://docs.ray.io/en/master/tune.html
+[6]: http://docs.ray.io/en/master
+[7]: http://docs.ray.io/en/master/api.html
 [8]: https://github.com/modin-project/modin
 [9]: https://ray-project.github.io/2017/10/15/fast-python-serialization-with-ray-and-arrow.html
 [10]: https://ray-project.github.io/2017/08/08/plasma-in-memory-object-store.html

doc/site/get_ray.html

@@ -25,7 +25,7 @@ layout: default
 </p>
 <ul>
 	<li>Ray Project <a href="https://ray.io">web site</a></li>
-	<li><a href="https://docs.ray.io/en/latest/">Documentation</a></li>
+	<li><a href="https://docs.ray.io/en/master/">Documentation</a></li>
 	<li><a href="https://github.com/ray-project/">GitHub project</a></li>
 	<li><a href="https://github.com/ray-project/tutorial">Tutorials</a></li>
 </ul>

doc/site/index.html

@@ -33,6 +33,6 @@ layout: default
 </ul>
 
 <p>
-	To get started, visit the Ray Project <a href="https://ray.io">web site</a>, <a href="https://docs.ray.io/en/latest/">documentation</a>, <a href="https://github.com/ray-project/">GitHub project</a>, or <a href="https://github.com/ray-project/tutorial">Tutorials</a>.
+	To get started, visit the Ray Project <a href="https://ray.io">web site</a>, <a href="https://docs.ray.io/en/master/">documentation</a>, <a href="https://github.com/ray-project/">GitHub project</a>, or <a href="https://github.com/ray-project/tutorial">Tutorials</a>.
 </p>
 </div>

doc/source/cluster/cloud.rst

@@ -302,4 +302,4 @@ Now that you have a working understanding of the cluster launcher, check out:
 Questions or Issues?
 --------------------
 
-.. include:: /_help.rst
+.. include:: /_help.rst

doc/source/cluster/index.rst

@@ -76,7 +76,7 @@ on each machine. To install Ray, follow the `installation instructions`_.
 
 To configure the Ray cluster to run Java code, you need to add the ``--code-search-path`` option. See :ref:`code_search_path` for more details.
 
-.. _`installation instructions`: http://docs.ray.io/en/latest/installation.html
+.. _`installation instructions`: http://docs.ray.io/en/master/installation.html
 
 Starting Ray on each machine
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/source/conf.py

@@ -119,10 +119,6 @@ extensions = [
 versionwarning_admonition_type = "tip"
 
 versionwarning_messages = {
-    "master": (
-        "This document is for the master branch. "
-        'Visit the <a href="/en/latest/">latest pip release documentation here</a>.'
-    ),
     "latest": (
         "This document is for the latest pip release. "
         'Visit the <a href="/en/master/">master branch documentation here</a>.'

doc/source/debugging.rst

@@ -97,4 +97,4 @@ This will print any ``RAY_LOG(DEBUG)`` lines in the source code to the
 
 
 .. _`issues`: https://github.com/ray-project/ray/issues
-.. _`Temporary Files`: http://docs.ray.io/en/latest/tempfile.html
+.. _`Temporary Files`: http://docs.ray.io/en/master/tempfile.html

doc/source/ray-dashboard.rst

@@ -137,7 +137,7 @@ You can view information for Ray objects in the memory tab. It is useful to debu
 
 One common cause of these memory errors is that there are objects which never go out of scope. In order to find these, you can go to the Memory View, then select to "Group By Stack Trace." This groups memory entries by their stack traces up to three frames deep. If you see a group which is growing without bound, you might want to examine that line of code to see if you intend to keep that reference around.
 
-Note that this is the same information as displayed in the `ray memory command <https://docs.ray.io/en/latest/memory-management.html#debugging-using-ray-memory>`_. For details about the information contained in the table, please see the `ray memory` documentation.
+Note that this is the same information as displayed in the `ray memory command <https://docs.ray.io/en/master/memory-management.html#debugging-using-ray-memory>`_. For details about the information contained in the table, please see the `ray memory` documentation.
 
 Inspect Memory Usage
 ~~~~~~~~~~~~~~~~~~~~
@@ -283,7 +283,7 @@ Memory
 
 **Object Size** Object Size of a Ray object in bytes.
 
-**Reference Type**: Reference types of Ray objects. Checkout the `ray memory command <https://docs.ray.io/en/latest/memory-management.html#debugging-using-ray-memory>`_ to learn each reference type.
+**Reference Type**: Reference types of Ray objects. Checkout the `ray memory command <https://docs.ray.io/en/master/memory-management.html#debugging-using-ray-memory>`_ to learn each reference type.
 
 **Call Site**: Call site where this Ray object is referenced, up to three stack frames deep.
 

doc/source/rllib-algorithms.rst

@@ -262,7 +262,7 @@ Deep Deterministic Policy Gradients (DDPG, TD3)
 -----------------------------------------------
 |pytorch| |tensorflow|
 `[paper] <https://arxiv.org/abs/1509.02971>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/rllib/agents/ddpg/ddpg.py>`__
-DDPG is implemented similarly to DQN (below). The algorithm can be scaled by increasing the number of workers or using Ape-X. The improvements from `TD3 <https://spinningup.openai.com/en/latest/algorithms/td3.html>`__ are available as ``TD3``.
+DDPG is implemented similarly to DQN (below). The algorithm can be scaled by increasing the number of workers or using Ape-X. The improvements from `TD3 <https://spinningup.openai.com/en/master/algorithms/td3.html>`__ are available as ``TD3``.
 
 .. figure:: dqn-arch.svg
 

doc/source/rllib-dev.rst

@@ -4,7 +4,7 @@ Contributing to RLlib
 Development Install
 -------------------
 
-You can develop RLlib locally without needing to compile Ray by using the `setup-dev.py <https://github.com/ray-project/ray/blob/master/python/ray/setup-dev.py>`__ script. This sets up links between the ``rllib`` dir in your git repo and the one bundled with the ``ray`` package. However if you have installed ray from source using [these instructions](https://docs.ray.io/en/latest/installation.html) then do not this as these steps should have already created this symlink. When using this script, make sure that your git branch is in sync with the installed Ray binaries (i.e., you are up-to-date on `master <https://github.com/ray-project/ray>`__ and have the latest `wheel <https://docs.ray.io/en/latest/installation.html>`__ installed.)
+You can develop RLlib locally without needing to compile Ray by using the `setup-dev.py <https://github.com/ray-project/ray/blob/master/python/ray/setup-dev.py>`__ script. This sets up links between the ``rllib`` dir in your git repo and the one bundled with the ``ray`` package. However if you have installed ray from source using [these instructions](https://docs.ray.io/en/master/installation.html) then do not this as these steps should have already created this symlink. When using this script, make sure that your git branch is in sync with the installed Ray binaries (i.e., you are up-to-date on `master <https://github.com/ray-project/ray>`__ and have the latest `wheel <https://docs.ray.io/en/master/installation.html>`__ installed.)
 
 API Stability
 -------------

doc/source/rllib-examples.rst

@@ -123,5 +123,5 @@ Community Examples
    Example of using the multi-agent API to model several `social dilemma games <https://arxiv.org/abs/1702.03037>`__.
 - `StarCraft2 <https://github.com/oxwhirl/smac>`__:
    Example of training in StarCraft2 maps with RLlib / multi-agent.
-- `Traffic Flow <https://berkeleyflow.readthedocs.io/en/latest/flow_setup.html>`__:
+- `Traffic Flow <https://berkeleyflow.readthedocs.io/en/master/flow_setup.html>`__:
    Example of optimizing mixed-autonomy traffic simulations with RLlib / multi-agent.

doc/source/troubleshooting.rst

@@ -105,7 +105,7 @@ on what Ray functionalities we use, let us see what cProfile's output might look
 like if our example involved Actors (for an introduction to Ray actors, see our
 `Actor documentation here`_).
 
-.. _`Actor documentation here`: http://docs.ray.io/en/latest/actors.html
+.. _`Actor documentation here`: http://docs.ray.io/en/master/actors.html
 
 Now, instead of looping over five calls to a remote function like in ``ex1``,
 let's create a new example and loop over five calls to a remote function

docker/ray/README.md

@@ -1,6 +1,6 @@
 ## About
 Default docker images for [Ray](https://github.com/ray-project/ray)! This includes
-everything needed to get started with running Ray! They work for both local development and *are ideal* for use with the [Ray Cluster Launcher](https://docs.ray.io/en/latest/cluster/launcher.html). [Find the Dockerfile here.](https://github.com/ray-project/ray/blob/master/docker/ray/Dockerfile)
+everything needed to get started with running Ray! They work for both local development and *are ideal* for use with the [Ray Cluster Launcher](https://docs.ray.io/en/master/cluster/launcher.html). [Find the Dockerfile here.](https://github.com/ray-project/ray/blob/master/docker/ray/Dockerfile)
 
 
 

python/asv.conf.json

@@ -7,7 +7,7 @@
     "project": "ray",
 
     // The project's homepage
-    "project_url": "http://docs.ray.io/en/latest/index.html",
+    "project_url": "http://docs.ray.io/en/master/index.html",
 
     // The URL or local path of the source code repository for the
     // project being benchmarked

python/ray/dashboard/client/src/pages/dashboard/logical-view/Actor.tsx

@@ -14,7 +14,7 @@ import { sum } from "../../../common/util";
 import ActorDetailsPane from "./ActorDetailsPane";
 
 const memoryDebuggingDocLink =
-  "https://docs.ray.io/en/latest/memory-management.html#debugging-using-ray-memory";
+  "https://docs.ray.io/en/master/memory-management.html#debugging-using-ray-memory";
 
 const useActorStyles = makeStyles((theme: Theme) =>
   createStyles({

python/ray/dashboard/client/src/pages/dashboard/tune/Tune.tsx

@@ -143,7 +143,7 @@ class Tune extends React.Component<
             You can use this tab to monitor Tune jobs, their statuses,
             hyperparameters, and more. For more information, read the
             documentation{" "}
-            <a href="https://docs.ray.io/en/latest/ray-dashboard.html#tune">
+            <a href="https://docs.ray.io/en/master/ray-dashboard.html#tune">
               here
             </a>
             .

python/ray/tune/README.rst

@@ -3,7 +3,7 @@ Tune: Scalable Hyperparameter Tuning
 
 Tune is a scalable framework for hyperparameter search with a focus on deep learning and deep reinforcement learning.
 
-User documentation can be `found here <http://docs.ray.io/en/latest/tune.html>`__.
+User documentation can be `found here <http://docs.ray.io/en/master/tune.html>`__.
 
 
 Tutorial

python/ray/tune/tests/ext_pytorch.py

@@ -17,7 +17,7 @@ accurate one. Often simple things like choosing a different learning rate or cha
 a network layer size can have a dramatic impact on your model performance.
 
 Fortunately, there are tools that help with finding the best combination of parameters.
-`Ray Tune <https://docs.ray.io/en/latest/tune.html>`_ is an industry standard tool for
+`Ray Tune <https://docs.ray.io/en/master/tune.html>`_ is an industry standard tool for
 distributed hyperparameter tuning. Ray Tune includes the latest hyperparameter search
 algorithms, integrates with TensorBoard and other analysis libraries, and natively
 supports distributed training through `Ray's distributed machine learning engine

python/ray/util/joblib/__init__.py

@@ -9,7 +9,7 @@ def register_ray():
     except ImportError:
         msg = ("To use the ray backend you must install ray."
                "Try running 'pip install ray'."
-               "See https://docs.ray.io/en/latest/installation.html"
+               "See https://docs.ray.io/en/master/installation.html"
                "for more information.")
         raise ImportError(msg)
 

python/ray/util/sgd/torch/examples/benchmarks/README.rst

@@ -3,7 +3,7 @@ Running benchmarks
 
 RaySGD provides comparable or better performance than other existing solutions for parallel or distributed training.
 
-You can run ``ray/python/ray/util/sgd/torch/examples/benchmarks/benchmark.py`` for benchmarking the RaySGD TorchTrainer implementation. To benchmark training on a multi-node multi-gpu cluster, you can use the `Ray Autoscaler <https://docs.ray.io/en/latest/autoscaling.html#aws>`_.
+You can run ``ray/python/ray/util/sgd/torch/examples/benchmarks/benchmark.py`` for benchmarking the RaySGD TorchTrainer implementation. To benchmark training on a multi-node multi-gpu cluster, you can use the `Ray Autoscaler <https://docs.ray.io/en/master/autoscaling.html#aws>`_.
 
 DISCLAIMER: RaySGD does not provide any custom communication primitives. If you see any performance issues, you may need to file them on the PyTorch github repository.
 

rllib/README.md

@@ -3,7 +3,7 @@ RLlib: Scalable Reinforcement Learning
 
 RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.
 
-For an overview of RLlib, see the [documentation](http://docs.ray.io/en/latest/rllib.html).
+For an overview of RLlib, see the [documentation](http://docs.ray.io/en/master/rllib.html).
 
 If you've found RLlib useful for your research, you can cite the [paper](https://arxiv.org/abs/1712.09381) as follows:
 

rllib/agents/pg/README.md

@@ -3,6 +3,6 @@ Policy Gradient (PG)
 
 An implementation of a vanilla policy gradient algorithm for TensorFlow and PyTorch. 
 
-**[Detailed Documentation](https://docs.ray.io/en/latest/rllib-algorithms.html#pg)**
+**[Detailed Documentation](https://docs.ray.io/en/master/rllib-algorithms.html#pg)**
 
 **[Implementation](https://github.com/ray-project/ray/blob/master/rllib/agents/pg/pg.py)**

rllib/agents/sac/README.md

@@ -5,6 +5,6 @@ Implementations of:
 
 Soft Actor-Critic Algorithm (SAC) and a discrete action extension. 
 
-**[Detailed Documentation](https://docs.ray.io/en/latest/rllib-algorithms.html#sac)**
+**[Detailed Documentation](https://docs.ray.io/en/master/rllib-algorithms.html#sac)**
 
 **[Implementation](https://github.com/ray-project/ray/blob/master/rllib/agents/sac/sac.py)**

rllib/agents/sac/sac.py

@@ -6,7 +6,7 @@ This file defines the distributed Trainer class for the soft actor critic
 algorithm.
 See `sac_[tf|torch]_policy.py` for the definition of the policy loss.
 
-Detailed documentation: https://docs.ray.io/en/latest/rllib-algorithms.html#sac
+Detailed documentation: https://docs.ray.io/en/master/rllib-algorithms.html#sac
 """
 
 import logging

rllib/contrib/README.rst

@@ -1,3 +1,3 @@
 Contributed algorithms, which can be run via ``rllib train --run=contrib/<alg_name>``
 
-See https://docs.ray.io/en/latest/rllib-dev.html for guidelines.
+See https://docs.ray.io/en/master/rllib-dev.html for guidelines.