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[rllib][asv] Support ASV for RLlib (ray-project#2304)
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richardliaw authored Jun 29, 2018
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15 changes: 12 additions & 3 deletions python/README-benchmarks.rst
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Expand Up @@ -8,22 +8,31 @@ You can run the benchmark suite by doing the following:

To run ASV inside docker, you can use the following command:
``docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA bash -c '/ray/test/jenkins_tests/run_asv.sh'``
``docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA bash -c '/ray/test/jenkins_tests/run_rllib_asv.sh'``


Visualizing Benchmarks
======================

To visualize benchmarks, you must copy the S3 bucket down to `$RAY_DIR/python`. Assuming asv is installed,
For visualizing regular Ray benchmarks, you must copy the S3 bucket down to `$RAY_DIR/python`.

.. code-block::
cd $RAY_DIR/python
aws s3 sync s3://$BUCKET/ASV/ .
Then, you can run:
For rllib, you must sync a _particular_ folder down to `$RLLIB_DIR (ray/python/ray/rllib)`.

.. code-block::
cd $RAY_DIR/python/ray/rllib
aws s3 sync s3://$BUCKET/RLLIB_RESULTS/ ./RLLIB_RESULTS
Then, in the directory, you can run:

.. code-block::
asv publish --no-pull
asv preview
This creates the directory and then launches a server.
This creates the directory and then launches a server at which you can visualize results.
141 changes: 141 additions & 0 deletions python/ray/rllib/asv.conf.json
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{
// The version of the config file format. Do not change, unless
// you know what you are doing.
"version": 1,

// The name of the project being benchmarked
"project": "rllib",

// The project's homepage
"project_url": "http://rllib.io",

// The URL or local path of the source code repository for the
// project being benchmarked
"repo": "../../../",

// List of branches to benchmark. If not provided, defaults to "master"
// (for git) or "default" (for mercurial).
"branches": ["master"], // for git
// "branches": ["default"], // for mercurial

// The DVCS being used. If not set, it will be automatically
// determined from "repo" by looking at the protocol in the URL
// (if remote), or by looking for special directories, such as
// ".git" (if local).
"dvcs": "git",

// The tool to use to create environments. May be "conda",
// "virtualenv" or other value depending on the plugins in use.
// If missing or the empty string, the tool will be automatically
// determined by looking for tools on the PATH environment
// variable.
"environment_type": "conda",

// timeout in seconds for installing any dependencies in environment
// defaults to 10 min
//"install_timeout": 600,

// the base URL to show a commit for the project.
"show_commit_url": "http://github.com/ray-project/ray/commit/",

// The Pythons you'd like to test against. If not provided, defaults
// to the current version of Python used to run `asv`.
"pythons": ["3.6"],

// The matrix of dependencies to test. Each key is the name of a
// package (in PyPI) and the values are version numbers. An empty
// list or empty string indicates to just test against the default
// (latest) version. null indicates that the package is to not be
// installed. If the package to be tested is only available from
// PyPi, and the 'environment_type' is conda, then you can preface
// the package name by 'pip+', and the package will be installed via
// pip (with all the conda available packages installed first,
// followed by the pip installed packages).
//
// "matrix": {
// "numpy": ["1.6", "1.7"],
// "six": ["", null], // test with and without six installed
// "pip+emcee": [""], // emcee is only available for install with pip.
// },

// Combinations of libraries/python versions can be excluded/included
// from the set to test. Each entry is a dictionary containing additional
// key-value pairs to include/exclude.
//
// An exclude entry excludes entries where all values match. The
// values are regexps that should match the whole string.
//
// An include entry adds an environment. Only the packages listed
// are installed. The 'python' key is required. The exclude rules
// do not apply to includes.
//
// In addition to package names, the following keys are available:
//
// - python
// Python version, as in the *pythons* variable above.
// - environment_type
// Environment type, as above.
// - sys_platform
// Platform, as in sys.platform. Possible values for the common
// cases: 'linux2', 'win32', 'cygwin', 'darwin'.
//
// "exclude": [
// {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows
// {"environment_type": "conda", "six": null}, // don't run without six on conda
// ],
//
// "include": [
// // additional env for python2.7
// {"python": "2.7", "numpy": "1.8"},
// // additional env if run on windows+conda
// {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""},
// ],

// The directory (relative to the current directory) that benchmarks are
// stored in. If not provided, defaults to "benchmarks"
"benchmark_dir": "tuned_examples/regression_tests",

// The directory (relative to the current directory) to cache the Python
// environments in. If not provided, defaults to "env"
// "env_dir": "env",

// The directory (relative to the current directory) that raw benchmark
// results are stored in. If not provided, defaults to "results".
"results_dir": "RLLIB_RESULTS",

// The directory (relative to the current directory) that the html tree
// should be written to. If not provided, defaults to "html".
// "html_dir": "html",

// The number of characters to retain in the commit hashes.
// "hash_length": 8,

// `asv` will cache wheels of the recent builds in each
// environment, making them faster to install next time. This is
// number of builds to keep, per environment.
// "wheel_cache_size": 0

// The commits after which the regression search in `asv publish`
// should start looking for regressions. Dictionary whose keys are
// regexps matching to benchmark names, and values corresponding to
// the commit (exclusive) after which to start looking for
// regressions. The default is to start from the first commit
// with results. If the commit is `null`, regression detection is
// skipped for the matching benchmark.
//
// "regressions_first_commits": {
// "some_benchmark": "352cdf", // Consider regressions only after this commit
// "another_benchmark": null, // Skip regression detection altogether
// }

// The thresholds for relative change in results, after which `asv
// publish` starts reporting regressions. Dictionary of the same
// form as in ``regressions_first_commits``, with values
// indicating the thresholds. If multiple entries match, the
// maximum is taken. If no entry matches, the default is 5%.
//
// "regressions_thresholds": {
// "some_benchmark": 0.01, // Threshold of 1%
// "another_benchmark": 0.5, // Threshold of 50%
// }
}
Empty file.
102 changes: 102 additions & 0 deletions python/ray/rllib/tuned_examples/regression_tests/regression_test.py
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#!/usr/bin/env python
"""
This class runs the regression YAMLs in the ASV format.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from collections import defaultdict
import numpy as np
import os
import yaml

import ray
from ray import tune


CONFIG_DIR = os.path.dirname(os.path.abspath(__file__))


def _evaulate_config(filename):
with open(os.path.join(CONFIG_DIR, filename)) as f:
experiments = yaml.load(f)
ray.init()
trials = tune.run_experiments(experiments)
results = defaultdict(list)
for t in trials:
results["time_total_s"] += [t.last_result.time_total_s]
results["episode_reward_mean"] += [t.last_result.episode_reward_mean]
results["training_iteration"] += [t.last_result.training_iteration]

return {k: np.median(v) for k, v in results.items()}


class Regression():
def setup_cache(self):
# We need to implement this in separate classes
# below so that ASV will register the setup/class
# as a separate test.
raise NotImplementedError

def teardown(self, *args):
ray.worker.cleanup()

def track_time(self, result):
return result["time_total_s"]

def track_reward(self, result):
return result["episode_reward_mean"]

def track_iterations(self, result):
return result["training_iteration"]


class TestCartPolePPO(Regression):
_file = "cartpole-ppo.yaml"

def setup_cache(self):
return _evaulate_config(self._file)


class TestCartPolePG(Regression):
_file = "cartpole-pg.yaml"

def setup_cache(self):
return _evaulate_config(self._file)


class TestPendulumDDPG(Regression):
_file = "pendulum-ddpg.yaml"

def setup_cache(self):
return _evaulate_config(self._file)


class TestCartPoleES(Regression):
_file = "cartpole-es.yaml"

def setup_cache(self):
return _evaulate_config(self._file)


class TestCartPoleDQN(Regression):
_file = "cartpole-dqn.yaml"

def setup_cache(self):
return _evaulate_config(self._file)


class TestCartPoleA3C(Regression):
_file = "cartpole-a3c.yaml"

def setup_cache(self):
return _evaulate_config(self._file)


class TestCartPoleA3CPyTorch(Regression):
_file = "cartpole-a3c-pytorch.yaml"

def setup_cache(self):
return _evaulate_config(self._file)
28 changes: 28 additions & 0 deletions test/jenkins_tests/run_rllib_asv.sh
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#!/usr/bin/env bash

# Cause the script to exit if a single command fails.
set -e

# Show explicitly which commands are currently running.
set -x

BUCKET_NAME=ray-integration-testing/ASV
COMMIT=$(cat /ray/git-rev)
RLLIB_RESULTS=RLLIB_RESULTS
RLLIB_RESULTS_DIR=/ray/python/ray/rllib/RLLIB_RESULTS
pip install awscli

# Install Ray fork of ASV
git clone https://github.com/ray-project/asv.git /tmp/asv/ || true
cd /tmp/asv/
pip install -e .

cd /ray/python/ray/rllib/
asv machine --machine jenkins
mkdir $RLLIB_RESULTS_DIR || true
aws s3 cp s3://$BUCKET_NAME/RLLIB_RESULTS/benchmarks.json $RLLIB_RESULTS_DIR/benchmarks.json || true

asv run --show-stderr --python=same --force-record-commit=$COMMIT

aws s3 cp $RLLIB_RESULTS_DIR/benchmarks.json s3://$BUCKET_NAME/RLLIB_RESULTS/benchmarks_$COMMIT.json
aws s3 sync $RLLIB_RESULTS_DIR/ s3://$BUCKET_NAME/RLLIB_RESULTS/

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