\n\n\n\n
This article demonstrates a basic configuration for CI\/CD on Gitlab. Continous Integration and Continous Development are terms that describe an end-to-end process from several changes in development to production environment. CI\/CD automates all the work for code integration such as integration test, unit test, and regression test, as well as deployment process with a set of predefined criteria. Hence, CI\/CD reduce manual effort in order to maintain the quality of the software.<\/p>\n\n\n\n This article focuses on inference part and you may need to add more layers for model development part. But, it almost the same process.<\/p>\n\n\n\n Please read these pages to grasp the concept of CI\/CD<\/p>\n\n\n\n <\/a><\/p>\n\n\n\n docs.gitlab.com<\/a><\/p>\n\n\n\n <\/a><\/p>\n\n\n\n cloud.google.com<\/a><\/p>\n\n\n\n Let\u2019s continue:<\/p>\n\n\n\n run Create Create Create Create Create Dockerfile<\/p>\n\n\n\n Make Create First thing first, please install flyctl on your computer by following this link https:\/\/fly.io\/docs\/hands-on\/install-flyctl\/<\/a><\/p>\n\n\n\n Then, do the authentication Then create your personal access token for Gitlab here https:\/\/fly.io\/user\/personal_access_tokens<\/a> save the token to your notepad, later we will add the token to gitlab environment.<\/p>\n\n\n\n Now, you need to create 2 app: staging and production.<\/p>\n\n\n\n Staging app<\/p>\n\n\n\n Production app<\/p>\n\n\n\n Don\u2019t forget to add access token fly.io to gitlab environment for deployment purposes. Add variable and named it as FLY_TOKEN.<\/p>\n\n\n\n *drum-roll*<\/p>\n\n\n\n Now, let\u2019s focus the main content here, configurating the CI\/CD pipline.<\/p>\n\n\n\n This is a simple gitlab-ci that runs every single push that you make to remote repo. What it does is when you push a change, 3 jobs will be triggered. docker-build, code-test, and production.<\/p>\n\n\n\n Let\u2019s dive in on how the things work.<\/p>\n\n\n\n Once you done:<\/p>\n\n\n\n Then, you can navigate to pipeline tab<\/p>\n\n\n\n As you can see, there are 3 green check mark that shows successful jobs had been run. If it fails, the icon will be red cross.<\/p>\n\n\n\n Now, you have created a simple pipeline.<\/p>\n\n\n\n Let\u2019s create a pipeline that usually used for ML API development.<\/p>\n\n\n\n We have 4 jobs:<\/p>\n\n\n\n Once you create merge request and merge into staging branch. it will deploy to staging app. If it is as expected, you can proceed to merge request to main branch, then approve. Once done, you can create tag to deploy into production web app.<\/p>\n\n\n\n That\u2019s more or less to setup CI\/CD on Gitlab. This may seems simplified, I will create more and more complex pipeline that involves MLOps such as model tracking, data versioning, model registry, model monitoring, etc. Hit the follow button and please connect on Linkedin at https:\/\/www.linkedin.com\/in\/chandraandreas\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" This article demonstrates a basic configuration for CI\/CD on Gitlab. Continous Integration and Continous Development are terms that describe an end-to-end process from several changes in development to production environment…. Read more »<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[51,19,158],"tags":[262,28,317],"class_list":["post-865","post","type-post","status-publish","format-standard","hentry","category-article","category-ml","category-mlops","tag-cd","tag-ci","tag-ci-cd-2"],"_links":{"self":[{"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/posts\/865","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/comments?post=865"}],"version-history":[{"count":1,"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/posts\/865\/revisions"}],"predecessor-version":[{"id":866,"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/posts\/865\/revisions\/866"}],"wp:attachment":[{"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/media?parent=865"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/categories?post=865"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tbekk.com\/devstream\/wp-json\/wp\/v2\/tags?post=865"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Prerequisites<\/strong><\/h1>\n\n\n\n
GitLab CI\/CD | GitLab<\/a><\/h2>\n\n\n\n
Learn how to use GitLab CI\/CD, the GitLab built-in Continuous Integration, Continuous Deployment, and Continuous\u2026<\/a><\/h3>\n\n\n\n
MLOps: Continuous delivery and automation pipelines in machine learning | Cloud Architecture Center\u2026<\/a><\/h2>\n\n\n\n
Last reviewed 2023-05-18 UTC This document discusses techniques for implementing and automating continuous integration\u2026<\/a><\/h3>\n\n\n\n
![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*uHkLtCb6TrBt-69vM_RdYw.jpeg\")
\n
![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*ebAY5bd4DfatUhSP50IC0A.png\")
<\/figure>\n\n\n\n\n
![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*y2mmbifjODtdGbXXaRgQuQ.png\")
<\/figure>\n\n\n\ngit clone git@gitlab.com:<your-username>\/iris-api.git<\/code><\/p>\n\n\n\n\n
![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:670\/1*WSMKEb1eZSCZr4qqUYDMFA.png\")
src\/main.py<\/code><\/p>\n\n\n\n\"\"\"Iris Web API Service.\"\"\"
from fastapi import FastAPI
from pydantic import BaseModel
import numpy as np
from src import distance, iris
dataset = iris.get_iris_data()
app = FastAPI()
class Item(BaseModel):
\"\"\"Input class for predict endpoint.
Args:
BaseModel (BaseModle): Inherited from pydantic
\"\"\"
sepal_length: float
sepal_width: float
petal_length: float
petal_width: float
@app.get(\"\/\")
def homepage():
\"\"\"Homepage for the web.
Returns:
str: Homepage
\"\"\"
return \"Homepage Iris Flower - tags 0.0.2\"
@app.post(\"\/predict\/\")
async def predict(item: Item):
\"\"\"Predict function for inference.
Args:
item (Item): dictionary of sepal dan petal data
Returns:
str: predict the target
\"\"\"
sepal_length = item.sepal_length
sepal_width = item.sepal_width
petal_length = item.petal_length
petal_width = item.petal_width
data_input = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
result = distance.calculate_manhattan(dataset, data_input)
return result<\/pre>\n\n\n\nsrc\/iris.py<\/code><\/p>\n\n\n\n\"\"\"Load iris dataset from scikit-learn.\"\"\"
from sklearn import datasets
def get_iris_data():
\"\"\"Load iris dataset.
Returns:
set: consists of X, y, feature names, and target_names
\"\"\"
iris = datasets.load_iris()
x_data = iris.data
y_label = iris.target
features_names = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]
target_names = iris.target_names
return x_data, y_label, features_names, target_names
if __name__ == \"__main__\":
x_data, y_label, features, target_names = get_iris_data()
print(\"X\", x_data)
print(\"y\", y_label)
print(\"features\", features)
print(\"target_names\", target_names)<\/pre>\n\n\n\nsrc\/distance.py<\/code><\/p>\n\n\n\n\"\"\"Distance module for calculating distance between data input and dataset.\"\"\"
import numpy as np
def calculate_manhattan(iris_data: np.ndarray, input_data: np.ndarray):
\"\"\"Calculate the distance between 2 vectors using manhattan distance.
Args:
dataset (np.ndarray): Iris dataset
input_data (np.ndarray): 1x4 matrix data input
Returns:
string: Return prediction
\"\"\"
x_data, y_label, _, target_names = iris_data
distance = np.sqrt(np.sum(np.abs(x_data - input_data), axis=1))
distance_index = np.argsort(distance)
y_pred = target_names[y_label[distance_index[0]]]
return y_pred
if __name__ == \"__main__\":
dataset = [
np.array([[4.9, 3.0, 1.4, 0.2], [4.9, 3.0, 1.4, 0.9]]),
[0, 0],
[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"],
[\"setosa\", \"versicolor\", \"virginica\"],
]
sample_data = np.array([[4.9, 3.0, 1.4, 0.2]])
print(calculate_manhattan(dataset, sample_data))<\/pre>\n\n\n\ntest\/test_distance.py<\/code><\/p>\n\n\n\nimport numpy as np
from src.iris import get_iris_data
from src.distance import calculate_manhattan
def test_calculate_manhattan():
dataset = get_iris_data()
input_data = np.array([[4.9, 3.0, 1.4, 0.2]])
result = calculate_manhattan(dataset, input_data)
assert result == 'setosa'<\/pre>\n\n\n\nFROM python:3.10
EXPOSE 8000
WORKDIR \/app
COPY . .
RUN pip install -r requirements.txt
ENTRYPOINT [\"uvicorn\", \"src.main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"]<\/pre>\n\n\n\nrequirements.txt<\/code><\/p>\n\n\n\n# python
pydoclint>=0.0.10
pylint>=2.17.0
black>=22.6.0
pydocstyle>=6.1.1
pytest>=7.1.2
# web app
fastapi>=0.98.0
uvicorn>=0.22.0
# models
numpy>=1.21.6
scikit-learn>=1.2.2<\/pre>\n\n\n\nfly.toml<\/code><\/p>\n\n\n\napp = \"iris-api-demo-stg\"
primary_region = \"sin\"
[build]
dockerfile = \"Dockerfile\"
[http_service]
internal_port = 8000
force_https = true
auto_stop_machines = true
auto_start_machines = true
min_machines_running = 0<\/pre>\n\n\n\nSetup Web App on Fly.io<\/h1>\n\n\n\n
flyctl auth login<\/code><\/p>\n\n\n\nflyctl launch --auto-confirm --copy-config --dockerfile Dockerfile --name iris-api-demo-stg --now --org personal --region sin<\/pre>\n\n\n\n
flyctl launch --auto-confirm --copy-config --dockerfile Dockerfile --name iris-api-demo--now --org personal --region sin<\/pre>\n\n\n\n
![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*VVUOKYTlRYrDAYVE9HxQjw.png\")
![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*u5haJQVk3uCf9D9k13pdPg.png\")
<\/figure>\n\n\n\nSetup CI\/CD<\/h1>\n\n\n\n
\n
gitlab-ci.yml<\/code> and call this v1<\/li>\n<\/ul>\n\n\n\nimage: python:latest
docker-build:
stage: build
script:
- echo \"Build Docker\"
code-test:
stage: test
script:
- echo \"Run Code Test\"
production:
stage: deploy
environment: production
script:
- echo \"Deploy to fly.io\"<\/pre>\n\n\n\nimage: python:latest<\/code> means that all these jobs run on top of docker image of python latest version which you can find on docker hub<\/a>.<\/p>\n\n\n\ndocker-build<\/code> is the name of the job. The name of the job can be anything and you can create numerous jobs in a single .yml file.<\/p>\n\n\n\nstage<\/code> means which stage this job falls into. There are 3 common stages in the CI\/CD pipeline, build, test and deploy.<\/p>\n\n\n\nenvironment<\/code> is used for specify which environment this job will run. You will get a list of jobs that has specific environments. This allow you to deploy which commit you want to redeploy. Hence, this makes easier if something went south in the staging or production environment.<\/p>\n\n\n\nscript<\/code> allows you to write a shell command in the container. Think like a set of script will run in the terminal.<\/p>\n\n\n\ngit add gitlab-ci.yml<\/code><\/p>\n\n\n\ngit commit -m \"add gitlab-ci.yml<\/code><\/p>\n\n\n\ngit push<\/code><\/p>\n\n\n\n![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*kTn70Z_nK0ZahtCoTw6wfQ.png\")
<\/figure>\n\n\n\n![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*jbLQBOiIcn0hYJinQEIniA.png\")
image: python:latest
code-check:
stage: build
only:
- merge_requests
script:
- echo \"Build Docker\"
- pip install -r requirements.txt
- pylint src --rcfile=.pylintrc
- black src --check
- pydocstyle src
code-test:
stage: test
only:
- merge_requests
script:
- echo \"Run Code Test\"
- pip install -r requirements.txt
- pytest
staging:
stage: deploy
environment: staging
only:
- staging
script:
- echo \"Deploy to fly.io in staging environment\"
- curl -L https:\/\/fly.io\/install.sh | sh
- bash
- \/root\/.fly\/bin\/flyctl deploy --app iris-api-demo-stg --access-token $FLY_TOKEN
production:
stage: deploy
environment: production
only:
- tags
script:
- echo \"Deploy to fly.io in production environment\"
- curl -L https:\/\/fly.io\/install.sh | sh
- \/root\/.fly\/bin\/flyctl deploy --app iris-api-demo --access-token $FLY_TOKEN<\/pre>\n\n\n\n\n
code-check<\/code> this job runs code quality check such as linting using pylint, formatter using black, and docstring using pydocstyle. This is used to make sure that the written code follow the guidelines. This job will only run on merge request. If you just push to the remote branch, it won\u2019t trigger this job.<\/li>\n\n\n\ncode-test<\/code> Then, we have code test, we have already created a simple unit test above in the test_main.py<\/code>This is to ensure that the module that we created run as expected.<\/li>\n\n\n\nstaging<\/code> this job will run if the merge request has been approved into staging branch. This will be automatically deployed to fly.io using stagging application. This allows you to do user acceptance test.<\/li>\n\n\n\nproduction<\/code> Finally, we have production job. The purpose is quite similar with staging one. This job will be triggered if you create a tag in the repository.<\/li>\n<\/ul>\n\n\n\n![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:1400\/1*1eEOPOi3ZvRLk3wMTJ0cBw.png\")
Conclusion<\/h1>\n\n\n\n