From 9da7bdcc8ed1700279da31509c40c1632ea05add Mon Sep 17 00:00:00 2001 From: Sumanth Ratna Date: Sat, 19 Sep 2020 03:30:45 -0400 Subject: [PATCH] Use master for links to docs in source (#10866) --- .github/PULL_REQUEST_TEMPLATE.md | 2 +- README.rst | 2 +- python/ray/setup-dev.py | 2 +- python/ray/tests/test_metrics.py | 2 +- rllib/README.md | 2 +- rllib/agents/dqn/apex.py | 2 +- rllib/agents/dqn/dqn.py | 2 +- rllib/agents/pg/pg.py | 2 +- rllib/agents/ppo/README.md | 6 +++--- rllib/agents/ppo/appo.py | 2 +- rllib/agents/ppo/ppo.py | 2 +- 11 files changed, 13 insertions(+), 13 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 5dbd4a177..35c053c90 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -13,7 +13,7 @@ ## Checks - [ ] I've run `scripts/format.sh` to lint the changes in this PR. -- [ ] I've included any doc changes needed for https://docs.ray.io/en/latest/. +- [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests diff --git a/README.rst b/README.rst index f0764e4eb..be800b98e 100644 --- a/README.rst +++ b/README.rst @@ -18,7 +18,7 @@ Ray is packaged with the following libraries for accelerating machine learning w There are also many `community integrations `_ with Ray, including `Dask`_, `MARS`_, `Modin`_, `Horovod`_, `Hugging Face`_, `Scikit-learn`_, and others. Check out the `full list of Ray distributed libraries here `_. Install Ray with: ``pip install ray``. For nightly wheels, see the -`Installation page `__. +`Installation page `__. .. _`Modin`: https://github.com/modin-project/modin .. _`Hugging Face`: https://huggingface.co/transformers/main_classes/trainer.html#transformers.Trainer.hyperparameter_search diff --git a/python/ray/setup-dev.py b/python/ray/setup-dev.py index 1d01e4490..b664ffd44 100755 --- a/python/ray/setup-dev.py +++ b/python/ray/setup-dev.py @@ -63,7 +63,7 @@ if __name__ == "__main__": print("Created links.\n\nIf you run into issues initializing Ray, please " "ensure that your local repo and the installed Ray are in sync " "(pip install -U the latest wheels at " - "https://docs.ray.io/en/latest/installation.html, " + "https://docs.ray.io/en/master/installation.html, " "and ensure you are up-to-date on the master branch on git).\n\n" "Note that you may need to delete the package symlinks when pip " "installing new Ray versions to prevent pip from overwriting files " diff --git a/python/ray/tests/test_metrics.py b/python/ray/tests/test_metrics.py index 9c492b5fc..e17c77ed0 100644 --- a/python/ray/tests/test_metrics.py +++ b/python/ray/tests/test_metrics.py @@ -402,7 +402,7 @@ def test_memory_dashboard(shutdown_only): """Test Memory table. These tests verify examples in this document. - 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 """ addresses = ray.init(num_cpus=2) webui_url = addresses["webui_url"].replace("127.0.0.1", "http://127.0.0.1") diff --git a/rllib/README.md b/rllib/README.md index 2259608b5..0c89e5eb3 100644 --- a/rllib/README.md +++ b/rllib/README.md @@ -27,4 +27,4 @@ If you've found RLlib useful for your research, you can cite the [paper](https:/ 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. 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. 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.) diff --git a/rllib/agents/dqn/apex.py b/rllib/agents/dqn/apex.py index 05577b5de..cc48c6820 100644 --- a/rllib/agents/dqn/apex.py +++ b/rllib/agents/dqn/apex.py @@ -9,7 +9,7 @@ Experience collection can scale to hundreds of CPU workers due to the distributed prioritization of experience prior to storage in replay buffers. Detailed documentation: -https://docs.ray.io/en/latest/rllib-algorithms.html#distributed-prioritized-experience-replay-ape-x +https://docs.ray.io/en/master/rllib-algorithms.html#distributed-prioritized-experience-replay-ape-x """ # noqa: E501 import collections diff --git a/rllib/agents/dqn/dqn.py b/rllib/agents/dqn/dqn.py index 04362a399..73d24e2bb 100644 --- a/rllib/agents/dqn/dqn.py +++ b/rllib/agents/dqn/dqn.py @@ -6,7 +6,7 @@ This file defines the distributed Trainer class for the Deep Q-Networks algorithm. See `dqn_[tf|torch]_policy.py` for the definition of the policies. Detailed documentation: -https://docs.ray.io/en/latest/rllib-algorithms.html#deep-q-networks-dqn-rainbow-parametric-dqn +https://docs.ray.io/en/master/rllib-algorithms.html#deep-q-networks-dqn-rainbow-parametric-dqn """ # noqa: E501 import logging diff --git a/rllib/agents/pg/pg.py b/rllib/agents/pg/pg.py index 2206c26e3..d4e774875 100644 --- a/rllib/agents/pg/pg.py +++ b/rllib/agents/pg/pg.py @@ -5,7 +5,7 @@ Policy Gradient (PG) This file defines the distributed Trainer class for policy gradients. See `pg_[tf|torch]_policy.py` for the definition of the policy loss. -Detailed documentation: https://docs.ray.io/en/latest/rllib-algorithms.html#pg +Detailed documentation: https://docs.ray.io/en/master/rllib-algorithms.html#pg """ from typing import Optional, Type diff --git a/rllib/agents/ppo/README.md b/rllib/agents/ppo/README.md index 6876c938e..1a11124f5 100644 --- a/rllib/agents/ppo/README.md +++ b/rllib/agents/ppo/README.md @@ -5,19 +5,19 @@ Implementations of: 1) Proximal Policy Optimization (PPO). - **[Detailed Documentation](https://docs.ray.io/en/latest/rllib-algorithms.html#ppo)** + **[Detailed Documentation](https://docs.ray.io/en/master/rllib-algorithms.html#ppo)** **[Implementation](https://github.com/ray-project/ray/blob/master/rllib/agents/ppo/ppo.py)** 2) Asynchronous Proximal Policy Optimization (APPO). - **[Detailed Documentation](https://docs.ray.io/en/latest/rllib-algorithms.html#appo)** + **[Detailed Documentation](https://docs.ray.io/en/master/rllib-algorithms.html#appo)** **[Implementation](https://github.com/ray-project/ray/blob/master/rllib/agents/ppo/appo.py)** 3) Decentralized Distributed Proximal Policy Optimization (DDPPO) - **[Detailed Documentation](https://docs.ray.io/en/latest/rllib-algorithms.html#decentralized-distributed-proximal-policy-optimization-dd-ppo)** + **[Detailed Documentation](https://docs.ray.io/en/master/rllib-algorithms.html#decentralized-distributed-proximal-policy-optimization-dd-ppo)** **[Implementation](https://github.com/ray-project/ray/blob/master/rllib/agents/ppo/ddppo.py)** diff --git a/rllib/agents/ppo/appo.py b/rllib/agents/ppo/appo.py index 5c0f8104b..f963cc7eb 100644 --- a/rllib/agents/ppo/appo.py +++ b/rllib/agents/ppo/appo.py @@ -7,7 +7,7 @@ of proximal policy optimization (APPO). See `appo_[tf|torch]_policy.py` for the definition of the policy loss. Detailed documentation: -https://docs.ray.io/en/latest/rllib-algorithms.html#appo +https://docs.ray.io/en/master/rllib-algorithms.html#appo """ from typing import Optional, Type diff --git a/rllib/agents/ppo/ppo.py b/rllib/agents/ppo/ppo.py index 4edc10ea9..2981b5077 100644 --- a/rllib/agents/ppo/ppo.py +++ b/rllib/agents/ppo/ppo.py @@ -6,7 +6,7 @@ This file defines the distributed Trainer class for proximal policy optimization. See `ppo_[tf|torch]_policy.py` for the definition of the policy loss. -Detailed documentation: https://docs.ray.io/en/latest/rllib-algorithms.html#ppo +Detailed documentation: https://docs.ray.io/en/master/rllib-algorithms.html#ppo """ import logging