[test] Check to make sure nothing major is lost

.gitignore

@@ -12,6 +12,9 @@
 /src/ray/object_manager/format/*_generated.h
 /src/ray/raylet/format/*_generated.h
 
+# Modin source files
+/python/ray/modin
+
 # Redis temporary files
 *dump.rdb
 

.travis.yml

@@ -131,6 +131,21 @@ script:
   # module is only found if the test directory is in the PYTHONPATH.
   - export PYTHONPATH="$PYTHONPATH:./test/"
 
+  # ray tune tests
+  - python python/ray/tune/test/dependency_test.py
+  - python -m pytest -v python/ray/tune/test/trial_runner_test.py
+  - python -m pytest -v python/ray/tune/test/trial_scheduler_test.py
+  - python -m pytest -v python/ray/tune/test/experiment_test.py
+  - python -m pytest -v python/ray/tune/test/tune_server_test.py
+  - python -m pytest -v python/ray/tune/test/ray_trial_executor_test.py
+  - python -m pytest -v python/ray/tune/test/automl_searcher_test.py
+
+  # ray rllib tests
+  - python -m pytest -v python/ray/rllib/test/test_catalog.py
+  - python -m pytest -v python/ray/rllib/test/test_filters.py
+  - python -m pytest -v python/ray/rllib/test/test_optimizers.py
+  - python -m pytest -v python/ray/rllib/test/test_evaluators.py
+
   - python -m pytest -v python/ray/test/test_global_state.py
   - python -m pytest -v python/ray/test/test_queue.py
   - python -m pytest -v python/ray/test/test_ray_init.py
@@ -153,24 +168,12 @@ script:
   - python -m pytest -v test/credis_test.py
   - python -m pytest -v test/node_manager_test.py
 
-  # ray tune tests
-  - python python/ray/tune/test/dependency_test.py
-  - python -m pytest -v python/ray/tune/test/trial_runner_test.py
-  - python -m pytest -v python/ray/tune/test/trial_scheduler_test.py
-  - python -m pytest -v python/ray/tune/test/experiment_test.py
-  - python -m pytest -v python/ray/tune/test/tune_server_test.py
-  - python -m pytest -v python/ray/tune/test/ray_trial_executor_test.py
-  - python -m pytest -v python/ray/tune/test/automl_searcher_test.py
-
-  # ray rllib tests
-  - python -m pytest -v python/ray/rllib/test/test_catalog.py
-  - python -m pytest -v python/ray/rllib/test/test_filters.py
-  - python -m pytest -v python/ray/rllib/test/test_optimizers.py
-  - python -m pytest -v python/ray/rllib/test/test_evaluators.py
-
   # ray temp file tests
   - python -m pytest -v test/tempfile_test.py
 
+  # modin test files
+  - python python/ray/test/test_modin.py
+
 deploy:
   - provider: s3
     access_key_id: AKIAJ2L7XDUSZVTXI5QA

.travis/install-dependencies.sh

@@ -24,7 +24,7 @@ if [[ "$PYTHON" == "2.7" ]] && [[ "$platform" == "linux" ]]; then
   wget https://repo.continuum.io/miniconda/Miniconda2-4.5.4-Linux-x86_64.sh -O miniconda.sh -nv
   bash miniconda.sh -b -p $HOME/miniconda
   export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.22 requests \
+  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.23.4 requests \
     feather-format lxml openpyxl xlrd py-spy setproctitle faulthandler pytest-timeout mock
 elif [[ "$PYTHON" == "3.5" ]] && [[ "$platform" == "linux" ]]; then
   sudo apt-get update
@@ -33,7 +33,7 @@ elif [[ "$PYTHON" == "3.5" ]] && [[ "$platform" == "linux" ]]; then
   wget https://repo.continuum.io/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh -O miniconda.sh -nv
   bash miniconda.sh -b -p $HOME/miniconda
   export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.22 requests \
+  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.23.4 requests \
     feather-format lxml openpyxl xlrd py-spy setproctitle pytest-timeout
 elif [[ "$PYTHON" == "2.7" ]] && [[ "$platform" == "macosx" ]]; then
   # check that brew is installed
@@ -50,7 +50,7 @@ elif [[ "$PYTHON" == "2.7" ]] && [[ "$platform" == "macosx" ]]; then
   wget https://repo.continuum.io/miniconda/Miniconda2-4.5.4-MacOSX-x86_64.sh -O miniconda.sh -nv
   bash miniconda.sh -b -p $HOME/miniconda
   export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.22 requests \
+  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.23.4 requests \
     feather-format lxml openpyxl xlrd py-spy setproctitle faulthandler pytest-timeout mock
 elif [[ "$PYTHON" == "3.5" ]] && [[ "$platform" == "macosx" ]]; then
   # check that brew is installed
@@ -67,7 +67,7 @@ elif [[ "$PYTHON" == "3.5" ]] && [[ "$platform" == "macosx" ]]; then
   wget https://repo.continuum.io/miniconda/Miniconda3-4.5.4-MacOSX-x86_64.sh -O miniconda.sh -nv
   bash miniconda.sh -b -p $HOME/miniconda
   export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.22 requests \
+  pip install -q cython==0.27.3 cmake tensorflow gym opencv-python pyyaml pandas==0.23.4 requests \
     feather-format lxml openpyxl xlrd py-spy setproctitle pytest-timeout
 elif [[ "$LINT" == "1" ]]; then
   sudo apt-get update

.travis/test-wheels.sh

@@ -59,7 +59,7 @@ if [[ "$platform" == "linux" ]]; then
   if [[ "$NUMBER_OF_WHEELS" != "5" ]]; then
     echo "Wrong number of wheels found."
     ls -l $ROOT_DIR/../.whl/
-    exit 1
+    exit 2
   fi
 
 elif [[ "$platform" == "macosx" ]]; then
@@ -94,5 +94,5 @@ elif [[ "$platform" == "macosx" ]]; then
   done
 else
   echo "Unrecognized environment."
-  exit 1
+  exit 3
 fi

README.rst

@@ -1,15 +1,19 @@
-Ray
-===
+.. raw:: html
+
+    <a href=http://ray.readthedocs.io/en/latest/index.html><img align="right" width="30%" src="https://github.com/devin-petersohn/ray/raw/docs/update_readme/doc/source/images/ray_logo.png"></a>
 
 .. image:: https://travis-ci.com/ray-project/ray.svg?branch=master
     :target: https://travis-ci.com/ray-project/ray
 
 .. image:: https://readthedocs.org/projects/ray/badge/?version=latest
     :target: http://ray.readthedocs.io/en/latest/?badge=latest
 
+.. image:: https://img.shields.io/badge/pypi-0.6.0-blue.svg
+    :target: https://pypi.org/project/ray/
+
 |
 
-Ray is a flexible, high-performance distributed execution framework.
+**Ray is a flexible, high-performance distributed execution framework.**
 
 
 Ray is easy to install: ``pip install ray``

cmake/Modules/ArrowExternalProject.cmake

@@ -15,10 +15,10 @@
 #  - PLASMA_SHARED_LIB
 
 set(arrow_URL https://github.com/apache/arrow.git)
-# The PR for this commit is https://github.com/apache/arrow/pull/2826. We
+# The PR for this commit is https://github.com/apache/arrow/pull/3061. We
 # include the link here to make it easier to find the right commit because
 # Arrow often rewrites git history and invalidates certain commits.
-set(arrow_TAG b4f7ed6d6ed5cdb6dd136bac3181a438f35c8ea0)
+set(arrow_TAG a667fca3b71772886bb2595986266d2039823dcc)
 
 set(ARROW_INSTALL_PREFIX ${CMAKE_CURRENT_BINARY_DIR}/external/arrow-install)
 set(ARROW_HOME ${ARROW_INSTALL_PREFIX})

doc/source/autoscaling.rst

@@ -78,6 +78,8 @@ You can use ``ray exec`` to conveniently run commands on clusters. Note that scr
 
 You can also use ``ray submit`` to execute Python scripts on clusters. This will ``rsync`` the designated file onto the cluster and execute it with the given arguments.
 
+.. code-block:: bash
+
     # Run a Python script in a detached tmux session
     $ ray submit cluster.yaml --tmux --start --stop tune_experiment.py
 

doc/source/conf.py

@@ -42,6 +42,7 @@
     "ray.core.generated.ClientTableData",
     "ray.core.generated.GcsTableEntry",
     "ray.core.generated.HeartbeatTableData",
+    "ray.core.generated.HeartbeatBatchTableData",
     "ray.core.generated.DriverTableData",
     "ray.core.generated.ErrorTableData",
     "ray.core.generated.ProfileTableData",

doc/source/installation.rst

@@ -10,7 +10,7 @@ You can install the latest stable version of Ray as follows.
 
 .. code-block:: bash
 
-  pip install -U ray
+  pip install -U ray  # also recommended: ray[debug]
 
 Trying snapshots from master
 ----------------------------
@@ -37,16 +37,16 @@ Here are links to the latest wheels (which are built off of master). To install
 ===================  ===================
 
 
-.. _`Linux Python 3.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp37-cp37m-manylinux1_x86_64.whl
-.. _`Linux Python 3.6`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp36-cp36m-manylinux1_x86_64.whl
-.. _`Linux Python 3.5`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp35-cp35m-manylinux1_x86_64.whl
-.. _`Linux Python 3.4`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp34-cp34m-manylinux1_x86_64.whl
-.. _`Linux Python 2.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp27-cp27mu-manylinux1_x86_64.whl
-.. _`MacOS Python 3.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp37-cp37m-macosx_10_6_intel.whl
-.. _`MacOS Python 3.6`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp36-cp36m-macosx_10_6_intel.whl
-.. _`MacOS Python 3.5`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp35-cp35m-macosx_10_6_intel.whl
-.. _`MacOS Python 3.4`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp34-cp34m-macosx_10_6_intel.whl
-.. _`MacOS Python 2.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.3-cp27-cp27m-macosx_10_6_intel.whl
+.. _`Linux Python 3.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp37-cp37m-manylinux1_x86_64.whl
+.. _`Linux Python 3.6`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp36-cp36m-manylinux1_x86_64.whl
+.. _`Linux Python 3.5`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp35-cp35m-manylinux1_x86_64.whl
+.. _`Linux Python 3.4`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp34-cp34m-manylinux1_x86_64.whl
+.. _`Linux Python 2.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp27-cp27mu-manylinux1_x86_64.whl
+.. _`MacOS Python 3.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp37-cp37m-macosx_10_6_intel.whl
+.. _`MacOS Python 3.6`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp36-cp36m-macosx_10_6_intel.whl
+.. _`MacOS Python 3.5`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp35-cp35m-macosx_10_6_intel.whl
+.. _`MacOS Python 3.4`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp34-cp34m-macosx_10_6_intel.whl
+.. _`MacOS Python 2.7`: https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.0-cp27-cp27m-macosx_10_6_intel.whl
 
 
 Building Ray from source

doc/source/rllib-algorithms.rst

@@ -133,10 +133,10 @@ Tuned examples: `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/pyt
    :start-after: __sphinx_doc_begin__
    :end-before: __sphinx_doc_end__
 
-Deep Q Networks (DQN, Rainbow)
-------------------------------
+Deep Q Networks (DQN, Rainbow, Parametric DQN)
+----------------------------------------------
 `[paper] <https://arxiv.org/abs/1312.5602>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/dqn/dqn.py>`__
-RLlib DQN is implemented using the SyncReplayOptimizer. The algorithm can be scaled by increasing the number of workers, using the AsyncGradientsOptimizer for async DQN, or using Ape-X. Memory usage is reduced by compressing samples in the replay buffer with LZ4. All of the DQN improvements evaluated in `Rainbow <https://arxiv.org/abs/1710.02298>`__ are available, though not all are enabled by default.
+RLlib DQN is implemented using the SyncReplayOptimizer. The algorithm can be scaled by increasing the number of workers, using the AsyncGradientsOptimizer for async DQN, or using Ape-X. Memory usage is reduced by compressing samples in the replay buffer with LZ4. All of the DQN improvements evaluated in `Rainbow <https://arxiv.org/abs/1710.02298>`__ are available, though not all are enabled by default. See also how to use `parametric-actions in DQN <rllib-models.html#variable-length-parametric-action-spaces>`__.
 
 Tuned examples: `PongDeterministic-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-dqn.yaml>`__, `Rainbow configuration <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-rainbow.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-basic-dqn.yaml>`__, `with Dueling and Double-Q <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-duel-ddqn.yaml>`__, `with Distributional DQN <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-dist-dqn.yaml>`__.
 

doc/source/rllib-env.rst

@@ -7,42 +7,56 @@ RLlib works with several different types of environments, including `OpenAI Gym
 
 **Compatibility matrix**:
 
-=============  ================  ==================  ===========  ==================
-Algorithm      Discrete Actions  Continuous Actions  Multi-Agent  Recurrent Policies
-=============  ================  ==================  ===========  ==================
-A2C, A3C        **Yes**           **Yes**             **Yes**      **Yes**
-PPO             **Yes**           **Yes**             **Yes**      **Yes**
-PG              **Yes**           **Yes**             **Yes**      **Yes**
-IMPALA          **Yes**           No                  **Yes**      **Yes**
-DQN, Rainbow    **Yes**           No                  **Yes**      No
-DDPG, TD3       No                **Yes**             **Yes**      No
-APEX-DQN        **Yes**           No                  **Yes**      No
-APEX-DDPG       No                **Yes**             **Yes**      No
-ES              **Yes**           **Yes**             No           No
-ARS             **Yes**           **Yes**             No           No
-=============  ================  ==================  ===========  ==================
-
-In the high-level agent APIs, environments are identified with string names. By default, the string will be interpreted as a gym `environment name <https://gym.openai.com/envs>`__, however you can also register custom environments by name:
+=============  =======================  ==================  ===========  ==================
+Algorithm      Discrete Actions         Continuous Actions  Multi-Agent  Recurrent Policies
+=============  =======================  ==================  ===========  ==================
+A2C, A3C        **Yes** `+parametric`_  **Yes**             **Yes**      **Yes**
+PPO             **Yes** `+parametric`_  **Yes**             **Yes**      **Yes**
+PG              **Yes** `+parametric`_  **Yes**             **Yes**      **Yes**
+IMPALA          **Yes** `+parametric`_  No                  **Yes**      **Yes**
+DQN, Rainbow    **Yes** `+parametric`_  No                  **Yes**      No
+DDPG, TD3       No                      **Yes**             **Yes**      No
+APEX-DQN        **Yes** `+parametric`_  No                  **Yes**      No
+APEX-DDPG       No                      **Yes**             **Yes**      No
+ES              **Yes**                 **Yes**             No           No
+ARS             **Yes**                 **Yes**             No           No
+=============  =======================  ==================  ===========  ==================
+
+.. _`+parametric`: rllib-models.html#variable-length-parametric-action-spaces
+
+You can pass either a string name or a Python class to specify an environment. By default, strings will be interpreted as a gym `environment name <https://gym.openai.com/envs>`__. Custom env classes must take a single ``env_config`` parameter in their constructor:
 
 .. code-block:: python
 
     import ray
-    from ray.tune.registry import register_env
     from ray.rllib.agents import ppo
 
-    def env_creator(env_config):
-        import gym
-        return gym.make("CartPole-v0")  # or return your own custom env
+    class MyEnv(gym.Env):
+        def __init__(self, env_config):
+            self.action_space = ...
+            self.observation_space = ...
+        ...
 
-    register_env("my_env", env_creator)
     ray.init()
-    trainer = ppo.PPOAgent(env="my_env", config={
-        "env_config": {},  # config to pass to env creator
+    trainer = ppo.PPOAgent(env=MyEnv, config={
+        "env_config": {},  # config to pass to env class
     })
 
     while True:
         print(trainer.train())
 
+You can also register a custom env creator function with a string name. This function must take a single ``env_config`` parameter and return an env instance:
+
+.. code-block:: python
+
+    from ray.tune.registry import register_env
+
+    def env_creator(env_config):
+        return MyEnv(...)  # return an env instance
+
+    register_env("my_env", env_creator)
+    trainer = ppo.PPOAgent(env="my_env")
+
 Configuring Environments
 ------------------------