Add distributed version of the code
This commit is contained in:
parent
3a971e5eba
commit
0999564601
GPG Key ID:
6758ACE46AA2A849
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@ -0,0 +1,12 @@
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.git
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.gitignore
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docker-compose.yml
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Dockerfile
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.dockerignore
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.editorconfig
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LICENSE
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output
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*.csv
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@ -0,0 +1,15 @@
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FROM python:3.8-slim
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WORKDIR /code
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COPY Pipfile Pipfile.lock ./
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RUN set -x \
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&& pip install pipenv \
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&& pipenv install --system --deploy
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CMD ["celery", "worker", "--app", "distributed.app", "-E", "-Ofair", "--loglevel=WARN"]
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ENV PYTHONPATH=/code \
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PYTHONUNBUFFERED="1" \
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C_FORCE_ROOT="1"
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COPY . ./
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4
Pipfile
4
Pipfile
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@ -6,8 +6,8 @@ verify_ssl = true
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[dev-packages]
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[packages]
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matplotlib = "*"
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scipy = "*"
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scipy = "==1.4.1"
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celery = {extras = ["librabbitmq", "redis"],version = "*"}
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[requires]
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python_version = "3"
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@ -0,0 +1,146 @@
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{
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"_meta": {
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"hash": {
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"sha256": "e6f3c835443673b396678699cc0401278a3c7827a9139dbdc05eff1fd769ecc6"
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},
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"pipfile-spec": 6,
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"requires": {
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"python_version": "3"
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},
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"sources": [
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{
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"name": "pypi",
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"url": "https://pypi.org/simple",
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"verify_ssl": true
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}
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]
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},
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"default": {
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"amqp": {
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"hashes": [
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"sha256:6e649ca13a7df3faacdc8bbb280aa9a6602d22fd9d545336077e573a1f4ff3b8",
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"sha256:77f1aef9410698d20eaeac5b73a87817365f457a507d82edf292e12cbb83b08d"
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],
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"version": "==2.5.2"
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},
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"billiard": {
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"hashes": [
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"sha256:01afcb4e7c4fd6480940cfbd4d9edc19d7a7509d6ada533984d0d0f49901ec82",
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"sha256:b8809c74f648dfe69b973c8e660bcec00603758c9db8ba89d7719f88d5f01f26"
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],
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"version": "==3.6.1.0"
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},
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"celery": {
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"extras": [
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"librabbitmq",
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"redis"
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],
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"hashes": [
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"sha256:7c544f37a84a5eadc44cab1aa8c9580dff94636bb81978cdf9bf8012d9ea7d8f",
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"sha256:d3363bb5df72d74420986a435449f3c3979285941dff57d5d97ecba352a0e3e2"
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],
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"index": "pypi",
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"version": "==4.4.0"
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},
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"kombu": {
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"hashes": [
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"sha256:2a9e7adff14d046c9996752b2c48b6d9185d0b992106d5160e1a179907a5d4ac",
|
||||
"sha256:67b32ccb6fea030f8799f8fd50dd08e03a4b99464ebc4952d71d8747b1a52ad1"
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],
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"version": "==4.6.7"
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},
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"librabbitmq": {
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"hashes": [
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"sha256:3116e40c02d4285b8dd69834e4cbcb1a89ea534ca9147e865f11d44e7cc56eea",
|
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"sha256:5cdfb473573396d43d54cef9e9b4c74fa3d1516da51d04a7b261f6ef4e0bd8be",
|
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"sha256:98e355f486964dadae7e8b51c9a60e9aa0653bbe27f6b14542687f305c4c3652",
|
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"sha256:c2a8113d3c831808d1d940fdf43e4882636a1efe2864df7ab3bb709a45016b37",
|
||||
"sha256:cd9cc09343b193d7cf2cff6c6a578061863bd986a4bdf38f922e9dc32e15d944",
|
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"sha256:ffa2363a860ab5dcc3ce3703247e05e940c73d776c03a3f3f9deaf3cf43bb96c"
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],
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"version": "==2.0.0"
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},
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"numpy": {
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"hashes": [
|
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"sha256:0a7a1dd123aecc9f0076934288ceed7fd9a81ba3919f11a855a7887cbe82a02f",
|
||||
"sha256:0c0763787133dfeec19904c22c7e358b231c87ba3206b211652f8cbe1241deb6",
|
||||
"sha256:3d52298d0be333583739f1aec9026f3b09fdfe3ddf7c7028cb16d9d2af1cca7e",
|
||||
"sha256:43bb4b70585f1c2d153e45323a886839f98af8bfa810f7014b20be714c37c447",
|
||||
"sha256:475963c5b9e116c38ad7347e154e5651d05a2286d86455671f5b1eebba5feb76",
|
||||
"sha256:64874913367f18eb3013b16123c9fed113962e75d809fca5b78ebfbb73ed93ba",
|
||||
"sha256:683828e50c339fc9e68720396f2de14253992c495fdddef77a1e17de55f1decc",
|
||||
"sha256:6ca4000c4a6f95a78c33c7dadbb9495c10880be9c89316aa536eac359ab820ae",
|
||||
"sha256:75fd817b7061f6378e4659dd792c84c0b60533e867f83e0d1e52d5d8e53df88c",
|
||||
"sha256:7d81d784bdbed30137aca242ab307f3e65c8d93f4c7b7d8f322110b2e90177f9",
|
||||
"sha256:8d0af8d3664f142414fd5b15cabfd3b6cc3ef242a3c7a7493257025be5a6955f",
|
||||
"sha256:9679831005fb16c6df3dd35d17aa31dc0d4d7573d84f0b44cc481490a65c7725",
|
||||
"sha256:a8f67ebfae9f575d85fa859b54d3bdecaeece74e3274b0b5c5f804d7ca789fe1",
|
||||
"sha256:acbf5c52db4adb366c064d0b7c7899e3e778d89db585feadd23b06b587d64761",
|
||||
"sha256:ada4805ed51f5bcaa3a06d3dd94939351869c095e30a2b54264f5a5004b52170",
|
||||
"sha256:c7354e8f0eca5c110b7e978034cd86ed98a7a5ffcf69ca97535445a595e07b8e",
|
||||
"sha256:e2e9d8c87120ba2c591f60e32736b82b67f72c37ba88a4c23c81b5b8fa49c018",
|
||||
"sha256:e467c57121fe1b78a8f68dd9255fbb3bb3f4f7547c6b9e109f31d14569f490c3",
|
||||
"sha256:ede47b98de79565fcd7f2decb475e2dcc85ee4097743e551fe26cfc7eb3ff143",
|
||||
"sha256:f58913e9227400f1395c7b800503ebfdb0772f1c33ff8cb4d6451c06cabdf316",
|
||||
"sha256:fe39f5fd4103ec4ca3cb8600b19216cd1ff316b4990f4c0b6057ad982c0a34d5"
|
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],
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"version": "==1.17.4"
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},
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"pytz": {
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"hashes": [
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"sha256:1c557d7d0e871de1f5ccd5833f60fb2550652da6be2693c1e02300743d21500d",
|
||||
"sha256:b02c06db6cf09c12dd25137e563b31700d3b80fcc4ad23abb7a315f2789819be"
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],
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"version": "==2019.3"
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},
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"redis": {
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"hashes": [
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"sha256:3613daad9ce5951e426f460deddd5caf469e08a3af633e9578fc77d362becf62",
|
||||
"sha256:8d0fc278d3f5e1249967cba2eb4a5632d19e45ce5c09442b8422d15ee2c22cc2"
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],
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"version": "==3.3.11"
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},
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"scipy": {
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"hashes": [
|
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"sha256:00af72998a46c25bdb5824d2b729e7dabec0c765f9deb0b504f928591f5ff9d4",
|
||||
"sha256:0902a620a381f101e184a958459b36d3ee50f5effd186db76e131cbefcbb96f7",
|
||||
"sha256:1e3190466d669d658233e8a583b854f6386dd62d655539b77b3fa25bfb2abb70",
|
||||
"sha256:2cce3f9847a1a51019e8c5b47620da93950e58ebc611f13e0d11f4980ca5fecb",
|
||||
"sha256:3092857f36b690a321a662fe5496cb816a7f4eecd875e1d36793d92d3f884073",
|
||||
"sha256:386086e2972ed2db17cebf88610aab7d7f6e2c0ca30042dc9a89cf18dcc363fa",
|
||||
"sha256:71eb180f22c49066f25d6df16f8709f215723317cc951d99e54dc88020ea57be",
|
||||
"sha256:770254a280d741dd3436919d47e35712fb081a6ff8bafc0f319382b954b77802",
|
||||
"sha256:787cc50cab3020a865640aba3485e9fbd161d4d3b0d03a967df1a2881320512d",
|
||||
"sha256:8a07760d5c7f3a92e440ad3aedcc98891e915ce857664282ae3c0220f3301eb6",
|
||||
"sha256:8d3bc3993b8e4be7eade6dcc6fd59a412d96d3a33fa42b0fa45dc9e24495ede9",
|
||||
"sha256:9508a7c628a165c2c835f2497837bf6ac80eb25291055f56c129df3c943cbaf8",
|
||||
"sha256:a144811318853a23d32a07bc7fd5561ff0cac5da643d96ed94a4ffe967d89672",
|
||||
"sha256:a1aae70d52d0b074d8121333bc807a485f9f1e6a69742010b33780df2e60cfe0",
|
||||
"sha256:a2d6df9eb074af7f08866598e4ef068a2b310d98f87dc23bd1b90ec7bdcec802",
|
||||
"sha256:bb517872058a1f087c4528e7429b4a44533a902644987e7b2fe35ecc223bc408",
|
||||
"sha256:c5cac0c0387272ee0e789e94a570ac51deb01c796b37fb2aad1fb13f85e2f97d",
|
||||
"sha256:cc971a82ea1170e677443108703a2ec9ff0f70752258d0e9f5433d00dda01f59",
|
||||
"sha256:dba8306f6da99e37ea08c08fef6e274b5bf8567bb094d1dbe86a20e532aca088",
|
||||
"sha256:dc60bb302f48acf6da8ca4444cfa17d52c63c5415302a9ee77b3b21618090521",
|
||||
"sha256:dee1bbf3a6c8f73b6b218cb28eed8dd13347ea2f87d572ce19b289d6fd3fbc59"
|
||||
],
|
||||
"index": "pypi",
|
||||
"version": "==1.4.1"
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||||
},
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"six": {
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"hashes": [
|
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"sha256:1f1b7d42e254082a9db6279deae68afb421ceba6158efa6131de7b3003ee93fd",
|
||||
"sha256:30f610279e8b2578cab6db20741130331735c781b56053c59c4076da27f06b66"
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||||
],
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"version": "==1.13.0"
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||||
},
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"vine": {
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"hashes": [
|
||||
"sha256:133ee6d7a9016f177ddeaf191c1f58421a1dcc6ee9a42c58b34bed40e1d2cd87",
|
||||
"sha256:ea4947cc56d1fd6f2095c8d543ee25dad966f78692528e68b4fada11ba3f98af"
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],
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"version": "==1.3.0"
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}
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},
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"develop": {}
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}
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#! /usr/bin/env python
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# -*- coding: utf-8 -*-
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# vim:fenc=utf-8
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#
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# Copyright © 2019 <pavle.portic@tilda.center>
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#
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# Distributed under terms of the BSD 3-Clause license.
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from celery import Celery
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from celery.signals import worker_ready
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app = Celery('distributed')
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app.config_from_object('distributed.celeryconfig')
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if app.conf.AM_I_SERVER:
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@worker_ready.connect
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def bootstrap(**kwargs):
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from .server import distributed_chunk
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delay_time = 2
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print(f'Getting ready to automatically seed computations in {delay_time} seconds...')
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kwargs = {
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'L1': app.conf.PENDULUM_L1,
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'L2': app.conf.PENDULUM_L2,
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'm1': app.conf.PENDULUM_M1,
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'm2': app.conf.PENDULUM_M2,
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'tmax': app.conf.PENDULUM_TMAX,
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'dt': app.conf.PENDULUM_DT,
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'theta_resolution': app.conf.PENDULUM_THETA_RESOLUTION,
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}
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distributed_chunk.apply_async(kwargs=kwargs, countdown=delay_time)
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if __name__ == '__main__':
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app.start()
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#! /usr/bin/env python
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# -*- coding: utf-8 -*-
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# vim:fenc=utf-8
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#
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# Copyright © 2019 <pavle.portic@tilda.center>
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#
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# Distributed under terms of the BSD 3-Clause license.
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from multiprocessing import cpu_count
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import os
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from kombu import Queue
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# Environment based settings
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MAX_CPU_CORES = os.getenv('MAX_CPU_CORES', cpu_count())
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AM_I_SERVER = (os.getenv('COMPUTER_TYPE') == 'server')
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# Parameters
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PENDULUM_L1 = int(os.getenv('PENDULUM_L1', 1))
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PENDULUM_L2 = int(os.getenv('PENDULUM_L2', 1))
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PENDULUM_M1 = int(os.getenv('PENDULUM_M1', 1))
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PENDULUM_M2 = int(os.getenv('PENDULUM_M2', 1))
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PENDULUM_TMAX = int(os.getenv('PENDULUM_TMAX', 30))
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PENDULUM_DT = float(os.getenv('PENDULUM_DT', 0.01))
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PENDULUM_THETA_RESOLUTION = int(os.getenv('PENDULUM_THETA_RESOLUTION', 6))
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PENDULUM_OUTPUT_FILE = os.getenv('PENDULUM_OUTPUT_FILE', '/output/dis.csv')
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# Concurrency settings
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CELERYD_CONCURRENCY = MAX_CPU_CORES
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# This ensures that each worker will only take one task at a time, when combined
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# with late acks. This is the recommended configuration for long-running tasks.
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# References:
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# * http://celery.readthedocs.org/en/latest/userguide/optimizing.html#prefetch-limits
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# * http://celery.readthedocs.org/en/latest/userguide/optimizing.html#reserve-one-task-at-a-time
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# * http://celery.readthedocs.org/en/latest/configuration.html#celeryd-prefetch-multiplier
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# * http://stackoverflow.com/questions/16040039/understanding-celery-task-prefetching
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# * https://bugs.launchpad.net/openquake-old/+bug/1092050
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# * https://wiredcraft.com/blog/3-gotchas-for-celery/
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# * http://www.lshift.net/blog/2015/04/30/making-celery-play-nice-with-rabbitmq-and-bigwig/
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CELERYD_PREFETCH_MULTIPLIER = 1
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# Task result backend settings
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CELERY_RESULT_BACKEND = 'redis://redis'
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# Message Routing
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CELERY_DEFAULT_QUEUE = 'worker'
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CELERY_DEFAULT_EXCHANGE = 'tasks'
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CELERY_DEFAULT_ROUTING_KEY = 'worker'
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if AM_I_SERVER:
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CELERY_QUEUES = (
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Queue('server', routing_key='server'),
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)
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else:
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CELERY_QUEUES = (
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Queue('worker', routing_key='worker'),
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)
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class ServerTasksRouter(object):
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def route_for_task(self, task, args=None, kwargs=None):
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if task.startswith('distributed.server.'):
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return {'queue': 'server'}
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return None
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CELERY_ROUTES = (
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ServerTasksRouter(),
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)
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||||
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||||
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||||
# Broker Settings
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BROKER_URL = "amqp://rabbitmq"
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CELERY_ACCEPT_CONTENT = ['pickle', 'json']
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# Task execution settings
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CELERY_MESSAGE_COMPRESSION = 'bzip2'
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CELERY_TASK_RESULT_EXPIRES = None
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CELERY_DISABLE_RATE_LIMITS = True
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CELERY_TRACK_STARTED = True
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# This ensures that the worker acks the task *after* it's completed.
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# If the worker crashes or gets killed mid-execution, the task will be returned
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# to the broker and restarted on another worker.
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# References:
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# * https://wiredcraft.com/blog/3-gotchas-for-celery/
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# * http://celery.readthedocs.org/en/latest/configuration.html#celery-acks-late
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# * http://celery.readthedocs.org/en/latest/faq.html#faq-acks-late-vs-retry
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CELERY_ACKS_LATE = True
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# Worker settings
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if AM_I_SERVER:
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CELERY_IMPORTS = ['distributed.server']
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else:
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CELERY_IMPORTS = ['distributed.worker']
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||||
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||||
# HACK: Prevents weird SymPy related memory leaks
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||||
CELERYD_MAX_TASKS_PER_CHILD = 100
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@ -0,0 +1,22 @@
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#! /usr/bin/env python
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# -*- coding: utf-8 -*-
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# vim:fenc=utf-8
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||||
#
|
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# Copyright © 2019 <pavle.portic@tilda.center>
|
||||
#
|
||||
# Distributed under terms of the BSD 3-Clause license.
|
||||
|
||||
import numpy as np
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||||
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||||
def iterate_both_theta(theta_resolution):
|
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search_space = np.linspace(0, 2 * np.pi, theta_resolution)
|
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for theta1 in search_space:
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for theta2 in search_space:
|
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yield theta1, theta2
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def iterate_theta(theta_resolution):
|
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search_space = np.linspace(0, 2 * np.pi, theta_resolution)
|
||||
for theta in search_space:
|
||||
yield theta
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
#! /usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# vim:fenc=utf-8
|
||||
#
|
||||
# Copyright © 2019 <pavle.portic@tilda.center>
|
||||
#
|
||||
# Distributed under terms of the BSD 3-Clause license.
|
||||
|
||||
import csv
|
||||
|
||||
from celery import chord
|
||||
|
||||
from .app import app
|
||||
from .worker import compute_single, compute_chunk
|
||||
from .common import iterate_theta, iterate_both_theta
|
||||
|
||||
|
||||
@app.task
|
||||
def distributed_single(L1, L2, m1, m2, tmax, dt, theta_resolution):
|
||||
return chord(
|
||||
(
|
||||
compute_single.s(L1, L2, m1, m2, tmax, dt, theta1_init, theta2_init)
|
||||
for theta1_init, theta2_init in iterate_both_theta(theta_resolution)
|
||||
),
|
||||
write_csv.s(),
|
||||
).delay()
|
||||
|
||||
|
||||
@app.task
|
||||
def distributed_chunk(L1, L2, m1, m2, tmax, dt, theta_resolution):
|
||||
return chord(
|
||||
(
|
||||
compute_chunk.s(L1, L2, m1, m2, tmax, dt, theta1_init, theta_resolution)
|
||||
for theta1_init in iterate_theta(theta_resolution)
|
||||
),
|
||||
write_csv.s(),
|
||||
).delay()
|
||||
|
||||
|
||||
def result_to_dict(theta1_init, result):
|
||||
return {
|
||||
'theta1_init': theta1_init,
|
||||
'theta2_init': result[0],
|
||||
'theta1': result[1],
|
||||
'theta2': result[2],
|
||||
}
|
||||
|
||||
|
||||
@app.task
|
||||
def write_csv(results):
|
||||
with open(app.conf.PENDULUM_OUTPUT_FILE, 'w', newline='') as csvfile:
|
||||
fieldnames = ['theta1_init', 'theta2_init', 'theta1', 'theta2']
|
||||
writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter=',')
|
||||
writer.writeheader()
|
||||
for chunk in sorted(results):
|
||||
for result in chunk[1]:
|
||||
writer.writerow(result_to_dict(chunk[0], result))
|
||||
|
||||
return len(results)
|
||||
|
|
@ -0,0 +1,57 @@
|
|||
#! /usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# vim:fenc=utf-8
|
||||
#
|
||||
# Copyright © 2019 <pavle.portic@tilda.center>
|
||||
#
|
||||
# Distributed under terms of the BSD 3-Clause license.
|
||||
|
||||
import numpy as np
|
||||
from scipy.integrate import odeint
|
||||
|
||||
from .app import app
|
||||
from .common import iterate_theta
|
||||
|
||||
|
||||
g = 9.81
|
||||
|
||||
|
||||
def deriv(y, t, L1, L2, m1, m2):
|
||||
theta1, z1, theta2, z2 = y
|
||||
c, s = np.cos(theta1 - theta2), np.sin(theta1 - theta2)
|
||||
theta1dot = z1
|
||||
z1dot = (m2 * g * np.sin(theta2) * c - m2 * s * (L1 * z1**2 * c + L2 * z2**2) - (m1 + m2) * g * np.sin(theta1)) / L1 / (m1 + m2 * s**2)
|
||||
theta2dot = z2
|
||||
z2dot = ((m1 + m2) * (L1 * z1**2 * s - g * np.sin(theta2) + g * np.sin(theta1) * c) + m2 * L2 * z2**2 * s * c) / L2 / (m1 + m2 * s**2)
|
||||
|
||||
return theta1dot, z1dot, theta2dot, z2dot
|
||||
|
||||
|
||||
def solve(L1, L2, m1, m2, tmax, dt, y0):
|
||||
t = np.arange(0, tmax + dt, dt)
|
||||
y = odeint(deriv, y0, t, args=(L1, L2, m1, m2))
|
||||
|
||||
return y[:, 0], y[:, 2]
|
||||
|
||||
|
||||
def compute(L1, L2, m1, m2, tmax, dt, theta1_init, theta2_init):
|
||||
y0 = np.array([theta1_init, 0, theta2_init, 0])
|
||||
theta1, theta2 = solve(L1, L2, m1, m2, tmax, dt, y0)
|
||||
|
||||
return theta2_init, theta1[-1], theta2[-1]
|
||||
|
||||
|
||||
@app.task
|
||||
def compute_single(L1, L2, m1, m2, tmax, dt, theta1_init, theta2_init):
|
||||
return compute(L1, L2, m1, m2, tmax, dt, theta1_init, theta2_init)
|
||||
|
||||
|
||||
@app.task
|
||||
def compute_chunk(L1, L2, m1, m2, tmax, dt, theta1_init, theta_resolution):
|
||||
return (
|
||||
theta1_init,
|
||||
[
|
||||
compute(L1, L2, m1, m2, tmax, dt, theta1_init, theta2_init)
|
||||
for theta2_init in iterate_theta(theta_resolution)
|
||||
]
|
||||
)
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
version: '3'
|
||||
services:
|
||||
worker:
|
||||
build: .
|
||||
environment:
|
||||
MAX_CPU_CORES: 8
|
||||
COMPUTER_TYPE: worker
|
||||
depends_on:
|
||||
- redis
|
||||
- rabbitmq
|
||||
|
||||
server:
|
||||
build: .
|
||||
environment:
|
||||
MAX_CPU_CORES: 1
|
||||
COMPUTER_TYPE: server
|
||||
PENDULUM_L1: 1
|
||||
PENDULUM_L2: 1
|
||||
PENDULUM_M1: 1
|
||||
PENDULUM_M2: 1
|
||||
PENDULUM_TMAX: 30
|
||||
PENDULUM_DT: 0.01
|
||||
PENDULUM_THETA_RESOLUTION: 6
|
||||
depends_on:
|
||||
- redis
|
||||
- rabbitmq
|
||||
volumes:
|
||||
- ./output:/output
|
||||
|
||||
redis:
|
||||
image: redis:alpine
|
||||
|
||||
rabbitmq:
|
||||
image: rabbitmq:alpine
|
||||
|
||||
Loading…
Reference in New Issue