use master link

.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>
             .

deploy/ray-operator/config/certmanager/certificate.yaml

@@ -1,6 +1,6 @@
 # The following manifests contain a self-signed issuer CR and a certificate CR.
 # More document can be found at https://docs.cert-manager.io
-# WARNING: Targets CertManager 0.11 check https://docs.cert-manager.io/en/latest/tasks/upgrading/index.html for breaking changes
+# WARNING: Targets CertManager 0.11 check https://docs.cert-manager.io/en/master/tasks/upgrading/index.html for breaking changes
 apiVersion: cert-manager.io/v1alpha2
 kind: Issuer
 metadata:

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/lm/lm-cluster.yaml

@@ -50,7 +50,7 @@ auth:
 # Provider-specific config for the head node, e.g. instance type. By default
 # Ray will auto-configure unspecified fields such as SubnetId and KeyName.
 # For more documentation on available fields, see:
-# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
+# http://boto3.readthedocs.io/en/master/reference/services/ec2.html#EC2.ServiceResource.create_instances
 head_node:
     InstanceType: m5.xlarge
     ImageId: ami-0b294f219d14e6a82 # Deep Learning AMI (Ubuntu) Version 21.0
@@ -67,7 +67,7 @@ head_node:
 # Provider-specific config for worker nodes, e.g. instance type. By default
 # Ray will auto-configure unspecified fields such as SubnetId and KeyName.
 # For more documentation on available fields, see:
-# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
+# http://boto3.readthedocs.io/en/master/reference/services/ec2.html#EC2.ServiceResource.create_instances
 worker_nodes:
     InstanceType: p3.2xlarge
     ImageId: ami-0b294f219d14e6a82 # Deep Learning AMI (Ubuntu) Version 21.0

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 @@
     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 @@ <h1>Getting Started with Ray</h1>
 </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 @@
 </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/advanced.rst

@@ -326,7 +326,7 @@ Notes
 * Have a look at ``cython_main.py``, ``cython_simple.pyx``, and ``setup.py`` for examples of how to call, define, and build Cython code, respectively. The Cython `documentation <http://cython.readthedocs.io/>`_ is also very helpful.
 * Several limitations come from Cython's own `unsupported <https://github.com/cython/cython/wiki/Unsupported>`_ Python features.
 * We currently do not support compiling and distributing Cython code to ``ray`` clusters. In other words, Cython developers are responsible for compiling and distributing any Cython code to their cluster (much as would be the case for users who need Python packages like ``scipy``).
-* For most simple use cases, developers need not worry about Python 2 or 3, but users who do need to care can have a look at the ``language_level`` Cython compiler directive (see `here <http://cython.readthedocs.io/en/latest/src/reference/compilation.html>`_).
+* For most simple use cases, developers need not worry about Python 2 or 3, but users who do need to care can have a look at the ``language_level`` Cython compiler directive (see `here <http://cython.readthedocs.io/en/master/src/reference/compilation.html>`_).
 
 Inspecting Cluster State
 ------------------------

doc/source/cluster/cloud.rst

@@ -23,7 +23,7 @@ AWS/GCP/Azure
     .. group-tab:: AWS
 
         First, install boto (``pip install boto3``) and configure your AWS credentials in ``~/.aws/credentials``,
-        as described in `the boto docs <http://boto3.readthedocs.io/en/latest/guide/configuration.html>`__.
+        as described in `the boto docs <http://boto3.readthedocs.io/en/master/guide/configuration.html>`__.
 
         Once boto is configured to manage resources on your AWS account, you should be ready to launch your cluster. The provided `ray/python/ray/autoscaler/aws/example-full.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/aws/example-full.yaml>`__ cluster config file will create a small cluster with an m5.large head node (on-demand) configured to autoscale up to two m5.large `spot workers <https://aws.amazon.com/ec2/spot/>`__.
 

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 @@ def __getattr__(cls, name):
 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/dask-on-ray.rst

@@ -17,7 +17,7 @@ Scheduler
 =========
 
 The Dask-Ray scheduler can execute any valid Dask graph, and can be used with
-any Dask `.compute() <https://docs.dask.org/en/latest/api.html#dask.compute>`__
+any Dask `.compute() <https://docs.dask.org/en/master/api.html#dask.compute>`__
 call.
 Here's an example:
 
@@ -61,7 +61,7 @@ Why use Dask on Ray?
       :ref:`shared-memory store <memory>`.
 
 Note that for execution on a Ray cluster, you should *not* use the
-`Dask.distributed <https://distributed.dask.org/en/latest/quickstart.html>`__
+`Dask.distributed <https://distributed.dask.org/en/master/quickstart.html>`__
 client; simply use plain Dask and its collections, and pass ``ray_dask_get``
 to ``.compute()`` calls. Follow the instructions for
 :ref:`using Ray on a cluster <using-ray-on-a-cluster>` to modify the
@@ -73,7 +73,7 @@ Dask-on-Ray is an ongoing project and is not expected to achieve the same perfor
 Callbacks
 =========
 
-Dask's `custom callback abstraction <https://docs.dask.org/en/latest/diagnostics-local.html#custom-callbacks>`__
+Dask's `custom callback abstraction <https://docs.dask.org/en/master/diagnostics-local.html#custom-callbacks>`__
 is extended with Ray-specific callbacks, allowing the user to hook into the
 Ray task submission and execution lifecycles.
 With these hooks, implementing Dask-level scheduler and task introspection,

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