Measurement & mixed states for quantum systems.
Notes on measurement for quantum systems.
Basic workflow of testing a dockerized Django & Postgres web app with Travis (continuous integration) & deployment to Heroku (continuous deployment).
Prerequisite:
1) This post assumes that the reader has accounts with Github, Travis & Heroku & already has the accounts configured. For example, linking Travis with Github, adding a Postgres server in Heroku & setting OS environment variables in Travis & Heroku websites.
2) Basic working knowledge of Django & Docker.
Code on my Github
Which copy of Postgres to use during the different stages in the workflow?
During development, we won’t be using a local copy of Postgres database server. The Docker’s copy of Postgres is used.
When testing in Travis, we don’t have to ask Travis for a copy of Postgres, we won’t be using Travis’s copy, we’ll be using the Docker’s copy.
During deployment, we have to use Heroku’s copy.
makemigrations
Always run docker-compose web run python manage.py makemigrations
before
deployment to Heroku or in our case, before pushing to Github.
The actual python manage.py migrate
for the Postgres server addon from
Heroku will be run in the Procfile
file.
Listing files & directories in a tree:
cd web_app_DPDTH
$ tree -a -I "CS50_web_dev|staticfiles|static|templates|LICENSE|README.md|__init__.py|settings_DPTH_.py|urls.py|wsgi.py|db.sqlite3|airline4_tests_.py|apps.py|migrations|views.py|models.py|flights.csv|manage.py|wait-for-it.sh|admin.py|.git|.travis_DPTH_.yml|__pycache__"
.
├── .travis.yml
├── .travis_DPDTH_.yml
├── .travis_old.yml
├── Dockerfile
├── Procfile
├── airline
│ └── settings.py
├── docker-compose.yml
├── docker_push.sh
├── flights
│ └── tests.py
├── heroku-container-release.sh
└── requirements.txt
As shown in the tree above, the 9 files that matter in the workflow:
1) tests.py
2) settings.py
3) requirements.txt
4) Dockerfile
5) docker-compose.yml
6) .travis.yml
7) docker_push.sh
8) heroku-container-release.sh
9) Procfile
We will look at the contents of each of the 9 files in the sections below.
tests.py
This is the test file that Travis will use for testing the app. You write whatever test you want for Travis to run with.
from django.db.models import Max
from django.test import Client, TestCase
from .models import Airport, Flight, Passenger
# Create your tests here.
class FlightsTestCase(TestCase):
def setUp(self):
# Create airports.
a1 = Airport.objects.create(code="AAA", city="City A")
a2 = Airport.objects.create(code="BBB", city="City B")
# Create flights.
Flight.objects.create(origin=a1, destination=a2, duration=100)
Flight.objects.create(origin=a1, destination=a1, duration=200)
Flight.objects.create(origin=a2, destination=a1, duration=300)
# 1
def test_departures_count(self):
a = Airport.objects.get(code="AAA")
self.assertEqual(a.departures.count(), 2)
# 2
def test_arrivals_count(self):
a = Airport.objects.get(code="AAA")
self.assertEqual(a.arrivals.count(), 2)
# 3
def test_valid_flight(self):
a1 = Airport.objects.get(code="AAA")
a2 = Airport.objects.get(code="BBB")
f = Flight.objects.get(origin=a1, destination=a2)
self.assertTrue(f.is_valid_flight())
settings.py
Under the database section, the DATABASES['default']
sets the default
database so the default database is the one connected by the OS environment
variable DATABASE_URL
, however if this is unavailable, we’ll use the one
defined with 'HOST': 'db'
.
This setup allows us to use 'HOST': 'db'
which is the Docker’s copy of
postgres during development phase & also during tesing phase with Travis
while using the Heroku’s copy during deployment which is provided by
connecting to the DATABASE_URL
.
The DATABASE_URL
, as an OS environment variable which is generated by
Heroku after a Database is added to the web app in the Heroku website.
Add and/or edit the following to the settings.py
file:
import django_heroku
import dj_database_url
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'whitenoise.middleware.WhiteNoiseMiddleware', # new
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'postgres',
'USER': 'postgres',
'PASSWORD': 'postgres',
'HOST': 'db', # Docker's copy of postgres
'PORT': 5432,
#'PORT': 5433,
}
}
DATABASE_URL = os.environ.get('DATABASE_URL')
db_from_env = dj_database_url.config(default=DATABASE_URL, conn_max_age=500, ssl_require=True)
DATABASES['default'].update(db_from_env)
STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
django_heroku.settings(locals())
requirements.txt
This file lets Docker & Travis know what packages are needed for the app.
This is needed in Dockerfile
& .travis.yml
files.
django>=2.0.11
psycopg2
psycopg2-binary
dj-database-url==0.5.0
gunicorn
whitenoise
django-heroku
pytz
sqlparse
Dockerfile
This contains the instructions for building a Docker image.
The CMD gunicorn airline.wsgi:application --bind 0.0.0.0:$PORT
tells
Docker to use gunicorn as the web server.
See here
for details.
FROM python:3
WORKDIR /usr/src/app
ADD requirements.txt /usr/src/app
RUN pip install -r requirements.txt
ADD . /usr/src/app
# collect static files
RUN python manage.py collectstatic --noinput
CMD gunicorn airline.wsgi:application --bind 0.0.0.0:$PORT
docker-compose.yml
This contains the instructions on how to run a Docker containers which is an instance of a Docker image.
Notice the sleep
delay introduced in the 2 command:
sections.
See here for details.
EDIT: The environment variables are added under db as newly required when testing on Travis.
version: '3'
services:
db:
image: postgres
environment:
- POSTGRES_DB=${POSTGRES_DB}
- POSTGRES_USER=${POSTGRES_USER}
- POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
migration:
build: .
command: bash -c 'while !</dev/tcp/db/5432; do sleep 1; done; python3 manage.py migrate'
volumes:
- .:/usr/src/app
depends_on:
- db
web:
build: .
# container_name: webapp-dpdth
image: webapp-dpdth
command: bash -c 'while !</dev/tcp/db/5432; do sleep 1; done; python3 manage.py runserver 0.0.0.0:8000'
volumes:
- .:/usr/src/app
ports:
- "8000:8000"
depends_on:
- db
- migration
.travis.yml
This file contains instructions for Travis. Notice that we’re not using the
Postgres from Travis because we’re using Postgres from Docker directly.
The postgresql
is therefore commented out under the services:
section.
Under script:
, we ask Travis to run the test using Docker with
the docker-compose
command.
Under deploy:
, we execute a docker_push.sh
script, more details in
the sections below.
The skip_cleanup: true
tells Travis not to remove any files that it
deems unnecessary after deployment. Travis does not have permission to do that
on Heroku anyway.
language: python
python:
- 3.6
services:
- docker
# - postgresql
install:
- pip install -r requirements.txt
script:
- docker-compose run web python manage.py test
deploy:
provider: script
script: bash docker_push.sh
skip_cleanup: true
on:
branch: master
Workflow for after testing with Travis to deployment to Heroku
The main workflow after testing to deployment is as such:
tag image -> push image to registry -> release image
We’ll see how to do that in the following script files:
1) docker_push.sh
2) heroku-container-release.sh
docker_push.sh
This file does several things listed as follows:
1) Login to the Heroku’s image registry.
2) tag
the source image webapp-dpdth:latest
to the target
image registry.heroku.com/webapp-dpdth/web
.
Replace webapp-dpdth
with your app name on Heroku.
3) Push the target image to Heroku’s registry if the branch tested on Travis is a master branch & that it’s not a PR.
4) Change ownership & permission of files to allow Travis to execute
the heroku-container-release.sh
script.
#!/bin/bash
sudo docker login --username $HEROKU_DOCKER_USERNAME --password $HEROKU_AUTH_TOKEN registry.heroku.com
sudo docker tag webapp-dpdth:latest registry.heroku.com/webapp-dpdth/web
if [ $TRAVIS_BRANCH == "master" ] && [ $TRAVIS_PULL_REQUEST == "false" ]; then sudo docker push registry.heroku.com/webapp-dpdth/web; fi
chmod +x heroku-container-release.sh
sudo chown $USER:docker ~/.docker
sudo chown $USER:docker ~/.docker/config.json
sudo chmod g+rw ~/.docker/config.json
./heroku-container-release.sh
heroku-container-release.sh
This file is for releasing a Docker image via Heroku’s API.
Replace webapp-dpdth
with your app name on Heroku.
#!/bin/bash
imageId=$(docker inspect registry.heroku.com/webapp-dpdth/web --format={{.Id}})
payload='{"updates":[{"type":"web","docker_image":"'"$imageId"'"}]}'
curl -n -X PATCH https://api.heroku.com/apps/webapp-dpdth/formation \
-d "$payload" \
-H "Content-Type: application/json" \
-H "Accept: application/vnd.heroku+json; version=3.docker-releases" \
-H "Authorization: Bearer $HEROKU_AUTH_TOKEN"
See here for details.
Procfile
This file is for Heroku. The command in the release:
section will run
after a Docker image is released. It will run the migrate
command
with --noinput
option. Without running migrate
, the database on
Heroku may not function correctly.
It also tells Heroku to deploy the web app using Gunicorn as the production server.
Note that airline
is the Django project name. It’s not the web app name
in the Django project & is also not the web app name in Heroku.
release: python manage.py migrate --noinput
web: gunicorn airline.wsgi
The deployed web app
With the above files in place, push to Github & Travis will start testing. After all tests passed, deployment starts. If there isn’t any failures, the web app will be running on:
https://webapp-dpdth.herokuapp.com
This link brings you to the admin page. It is using the Heroku’s copy of Postgres.
This link brings you to my built log in Travis.com which shows how a successful test/deploy built looks like.
Web security:
Please note that web security has not been throughly consider in this basic workflow describe above. Do NOT simply use the above workflow for production.
For example the SECRET_KEY
in the settings.py
isn’t dealt with at all
and web security is really beyond the scope of this post.
Notes on measurement for quantum systems.
Notes on quantum states as a generalization of classical probabilities.
The location of ray_results folder in colab when using RLlib &/or tune.
My attempt to implement a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning).
Ray (0.8.2) RLlib trainer common config from:
How to calculate dimension of output from a convolution layer?
Changing Google drive directory in Colab.
Notes on the probability for linear regression (Bayesian)
Notes on the math for RNN back propagation through time(BPTT), part 2. The 1st derivative of \(h_t\) with respect to \(h_{t-1}\).
Notes on the math for RNN back propagation through time(BPTT).
Filter rows with same column values in a Pandas dataframe.
Building & testing custom Sagemaker RL container.
Demo setup for simple (reinforcement learning) custom environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).
Basic workflow of testing a Django & Postgres web app with Travis (continuous integration) & deployment to Heroku (continuous deployment).
Basic workflow of testing a dockerized Django & Postgres web app with Travis (continuous integration) & deployment to Heroku (continuous deployment).
Introducing a delay to allow proper connection between dockerized Postgres & Django web app in Travis CI.
Creating & seeding a random policy class in RLlib.
A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another in a CDA (continuous double auction).
This post demonstrate how setup & access Tensorflow graphs.
This post demonstrates how to create customized functions to bundle commands in a .bash_profile file on Mac.
This post documents my implementation of the Random Network Distillation (RND) with Proximal Policy Optimization (PPO) algorithm. (continuous version)
This post documents my implementation of the Distributed Proximal Policy Optimization (Distributed PPO or DPPO) algorithm. (Distributed continuous version)
This post documents my implementation of the A3C (Asynchronous Advantage Actor Critic) algorithm (Distributed discrete version).
This post documents my implementation of the A3C (Asynchronous Advantage Actor Critic) algorithm. (multi-threaded continuous version)
This post documents my implementation of the A3C (Asynchronous Advantage Actor Critic) algorithm (discrete). (multi-threaded discrete version)
This post demonstrates how to accumulate gradients with Tensorflow.
This post demonstrates a simple usage example of distributed Tensorflow with Python multiprocessing package.
This post documents my implementation of the N-step Q-values estimation algorithm.
This post demonstrates how to use the Python’s multiprocessing package to achieve parallel data generation.
This post provides a simple usage examples for common Numpy array manipulation.
This post documents my implementation of the Dueling Double Deep Q Network with Priority Experience Replay (Duel DDQN with PER) algorithm.
This post documents my implementation of the Dueling Double Deep Q Network (Dueling DDQN) algorithm.
This post documents my implementation of the Double Deep Q Network (DDQN) algorithm.
This post documents my implementation of the Deep Q Network (DQN) algorithm.