{"id":7410,"date":"2023-09-11T09:12:12","date_gmt":"2023-09-11T17:12:12","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=7410"},"modified":"2025-04-24T17:14:18","modified_gmt":"2025-04-24T17:14:18","slug":"mlops-pipeline-with-gitlab-in-minutes","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/mlops-pipeline-with-gitlab-in-minutes\/","title":{"rendered":"MLOps Pipeline with GitLab in Minutes"},"content":{"rendered":"\n\n\n\n\n
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Photo by Sven Huls: https:\/\/www.pexels.com\/photo\/brown-wooden-bridge-over-green-trees-3801347\/<\/a><\/figcaption><\/figure>\n

In this article, you\u2019ll learn deploy your Flask application to Heroku with GitLab in minutes! You\u2019ll implement the MLOps pipeline, with continuous integration (CI), and continuous deployment (CD), with easy-to-follow, step-by-step explanations. You\u2019ll automate the MLOps CI-CD pipeline with GitLab seamlessly.<\/p>\n

Contents<\/h1>\n

Introduction<\/strong><\/p>\n

GitLab<\/strong><\/p>\n

Continuous Integration (CI) Part<\/strong><\/p>\n

GitLab Preparation<\/strong><\/p>\n

Continuous Integration (CI) with GitLab<\/strong><\/p>\n

.gitlab-ci.yml File<\/strong><\/p>\n

Continuous Deployment (CD) Part<\/strong><\/p>\n

CD part of .gitlab-ci.yml File<\/strong><\/p>\n

Conclusion<\/strong><\/p>\n

Introduction<\/h1>\n

In this article, we will discuss implementing an end-to-end MLOps pipeline using GitLab and Heroku. Before moving further, let\u2019s learn a little bit about GitLab.<\/p>\n

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https:\/\/about.gitlab.com\/<\/a><\/figcaption>\n<\/figure>\n

GitLab<\/strong><\/h1>\n

GitLab\u2019s web page starts with the slogan \u201cThe One DevOps Platform.\u201d It continues with the explanation, \u201cFrom planning to production, bring teams together in one application. Ship secure code faster, deploy to any cloud and drive business results. Automatically verify your code with powerful continuous integration (CI) capabilities.\u201d (https:\/\/about.gitlab.com\/<\/a>)<\/p>\n

This is a great introduction to what you can use GitLab for. Without further ado, let\u2019s see GitLab\u2019s CI-CD capabilities in action!<\/p>\n

Continuous Integration (CI)<\/h1>\n

For the CI part, I used the heart-failure dataset<\/a> from Kaggle. You can find my detailed discussion on EDA and ML models on here<\/strong><\/a>. Based on the final Catboost model, a basic Flask App has been developed. But before pushing to GitHub, let\u2019s be sure that everything is okay in the local environment.<\/p>\n

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Image by Author<\/figcaption>\n<\/figure>\n

As we can see above, everything is working well.<\/p>\n

GitLab Preparation<\/h2>\n

After getting a free account on the GitLab web page, we can start to write our new project on GitLab. First, select a new project, give a name to the project, and then we can start the project.<\/p>\n

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Now we can push our directory to GitLab by using the following provided Git Commands from the GitLab page.<\/p>\n

git init --initial-branch=main\n\ngit remote add origin git@gitlab.com:kb1907\/gitlab-ci-cd.git\n\ngit add .\n\ngit commit -m \"Initial commit\"\n\ngit push -u origin main<\/span><\/pre>\n
Before using the GitLab CI pipeline, however, we need to install GitLab Runner<\/strong><\/a>. \u201cGitLab Runner is an application that works with GitLab CI\/CD to run jobs in a pipeline.\u201d You can find the installation information here<\/strong><\/a>. <\/strong>For this tutorial, I\u2019ll use Homebrew to install the GitLab Runner.<\/p>\n
brew install gitlab-runner<\/span><\/pre>\n
Then we will install GitLab Runner as a service and start it.<\/p>\n
brew services start gitlab-runner<\/span><\/pre>\n
GitLab Runner is now installed and running. We will then register GitLab Runner.<\/p>\n
sudo gitlab-runner register<\/span><\/pre>\n
After entering your system password, you\u2019ll get several questions. To answer these questions, go to your project\u2019s GitLab website. Go to settings and select CI-CD, as shown below:<\/p>\n
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Then select Runner and copy the URL address as shown below:<\/p>\n
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Image captured by Author<\/figcaption>\n<\/figure>\n
Paste the URL address as an answer to: gitlab-ci coordinator URL<\/code> and provide a token from the same page as an answer to: token<\/code> for this runner<\/p>\n
Next, describe your runner. I gave gitlab-heroku<\/code> as a description in the example. Then it will ask for the tag name<\/strong>, which is very important, because we will use it on CI.yml file. This tag will map our project to the runner. I used the same name for this tag, gitlab-heroku<\/code>. Finally, it will ask for the executor. I selected \u201cshell\u201d.<\/p>\n
That\u2019s it. The runner is registered successfully! Let\u2019s verify it.<\/p>\n
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Image captured by Author.<\/figcaption>\n<\/figure>\n
Now we can start this runner:<\/p>\n
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Image captured by Author<\/figcaption>\n<\/figure>\n
The runner is actively listening to any commit in our GitLab repository.<\/p>\n
Continuous Integration (CI) With GitLab<\/h1>\n
Now, we can start to implement the continuous integration (CI) part of the pipeline. The first thing we need to do is write a .yml file. Let\u2019s do that.<\/p>\n
.gitlab-ci.yml file<\/h2>\n
To start the continuous integration part of the pipeline, we need to inform GitLab of each step of the pipeline with the specific commands. Remember that the .yml file must be named as .gitlab-ci.yml<\/strong><\/code><\/p>\n
image: python:3.9-slim\n\nstages:\n  - build\n  - test\n  - deploy\n\nbuild:\n  stage: build\n  tags:\n    - \"gitlab-heroku\"\n  script:\n      - pip install -r requirements.txt\n\nrun_tests:\n  stage: test\n  tags:\n    - \"gitlab-heroku\"\n  script:\n    - python -m pytest -vv test.py<\/span><\/pre>\n
Let\u2019s look at the .yml file in detail:<\/p>\n
image: python:3.9-slim<\/span><\/pre>\n
The .yml file will use Python version 3.9 version.<\/p>\n
stages:\n  - build\n  - test\n  - deploy<\/span><\/pre>\n
The .yml file defines three stages. In the CI part, we will use the first two (build & test):<\/p>\n
build:\n  stage: build\n  tags:\n    - \"gitlab-heroku\"\n  script:\n      - pip install -r requirements.txt<\/span><\/pre>\n
In the build stage, we first defined the stage name as build<\/code>. Then we used tags as our runner\u2019s tag, which is gitlab-heroku<\/code>. Without this, the CI pipeline will fail. And finally, we gave a command to install the required libraries.<\/p>\n
run_tests:\n  stage: test\n  tags:\n    - \"gitlab-heroku\"\n  script:\n    - python -m pytest -vv test.py<\/span><\/pre>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Most projects fail before they get to production. Check out our free ebook to learn how to implement an MLOps lifecycle to better monitor, train, and deploy your machine learning models to increase output and iteration<\/a>.<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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At the test stage, we again used tags as our runner\u2019s tag, which is gitlab-heroku<\/code>.<\/p>\n
Although it seems like a repetition, in each step we are running one stage of the pipeline, and each stage needs a runner. For that, we have to define the runner for each stage.<\/p>\n
Before pushing the .yml file and starting the continuous integration part of the pipeline, let\u2019s verify our runner from the GitLab website:<\/p>\n
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Image captured by Author<\/figcaption>\n<\/figure>\n
As you can see, everything seems fine and working. So, let\u2019s start the continuous integration part of the pipeline and check the results.<\/p>\n
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Image captured by Author<\/figcaption>\n<\/figure>\n
The pipeline started with the build stage:<\/p>\n
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Now the pipeline continues with the second stage:<\/p>\n
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Image captured by Author<\/figcaption>\n<\/figure>\n
Both of the stages completed successfully! We can now move on to the continuous deployment (CD) part of the MLOps pipeline.<\/p>\n
Continuous Deployment (CD) With Heroku<\/h1>\n
For this part, we will use Heroku as a cloud application platform. Using our Flask application in the Heroku environment, we need to create Procfile and put this one line command in it.<\/p>\n
web gunicorn app:app<\/span><\/pre>\n
Let\u2019s go to the Heroku website and create a new application:<\/p>\n
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Image captured by Author.<\/figcaption>\n<\/figure>\n
We need to get an authentication token from Heroku to connect directly from GitLab and start continuous deployment. From Account settings \/ Applications<\/code>, we selected create authorization option.<\/p>\n
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We need this token when creating variables in GitLab.<\/p>\n
Variables store information like passwords or tokens.<\/p>\n
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We will add two variables:<\/p>\n
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  1. HEROKU_STAGING_APP<\/code>: Heroku app\u2019s name<\/li>\n
  2. HEROKU_STAGING_API_KEY<\/code>: Token which we got previously.<\/li>\n<\/ol>\n
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    Image captured by Author<\/figcaption>\n<\/figure>\n
    CD part of .gitlab-ci.yml file<\/h1>\n
    We can finalize our .yml file by adding the last stage:<\/p>\n
    deploy:\n  stage: deploy\n  image: ruby:latest\n  tags:\n    - \"gitlab-heroku\"\n  script:\n    - gem install dpl\n    - dpl --provider=heroku --app=$HEROKU_STAGING_APP --api-key=$HEROKU_STAGING_API_KEY\n  only:\n    - main<\/span><\/pre>\n
    Let\u2019s look at this part in detail:<\/p>\n
    deploy:\n  stage: deploy\n  image: ruby:latest\n  tags:\n    - \"gitlab-heroku\"<\/span><\/pre>\n
    At the deployment stage, we will use Ruby to deploy our app to Heroku. For that, we will get the Ruby image.<\/p>\n
    We used tags as our runner\u2019s tag, which is gitlab-heroku<\/code>.<\/p>\n
    script:\n    - gem install dpl\n    - dpl --provider=heroku --app=$HEROKU_STAGING_APP --api-key=$HEROKU_STAGING_API_KEY<\/span><\/pre>\n
    Ruby will install the dpl<\/code> library. dpl<\/code> will deploy our app from GitLab to Heroku by using previously defined variables.<\/p>\n
    only:\n    - main<\/span><\/pre>\n
    Whenever there is a change on the main branch, it will deploy. We are now ready to start the CD part of the pipeline.<\/p>\n
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    Image captured by Author<\/figcaption>\n<\/figure>\n
    As we can see, everything on the GitLab side is fine. Let\u2019s take a look at the Heroku website and see our app:<\/p>\n
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    Image captured by Author<\/figcaption>\n<\/figure>\n
    We did it!<\/p>\n
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    <\/picture><\/div>\n<\/div>\n
    Photo by Marc Najera<\/a> on Unsplash<\/a><\/figcaption>\n<\/figure>\n
    Conclusion<\/h1>\n
    In this article, we established the MLOps CI-CD pipeline by using the GitLab platform with Heroku. We automated the MLOps CI-CD pipeline with GitLab in minutes. The GitLab Runner preparation took some time, but other than that, everything worked seamlessly. I hope that it helps.<\/p>\n
    By the way, if you like the topic, you can show it by supporting with a \ud83d\udc4f!<\/strong><\/p>\n
    Feel free to leave a comment and thanks for your time.<\/p>\n
    All the best \ud83e\udd18<\/p>\n
    The code can be downloaded here<\/strong><\/a>.<\/p>\n
    If you enjoy reading my content, please consider following me<\/em><\/a>. Also, you can support me and other writers by subscribing to Medium<\/strong><\/a>. Using my referral link will not cost you extra.<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
    Photo by Sven Huls: https:\/\/www.pexels.com\/photo\/brown-wooden-bridge-over-green-trees-3801347\/ In this article, you\u2019ll learn deploy your Flask application to Heroku with GitLab in minutes! You\u2019ll implement the MLOps pipeline, with continuous integration (CI), and continuous deployment (CD), with easy-to-follow, step-by-step explanations. You\u2019ll automate the MLOps CI-CD pipeline with GitLab seamlessly. Contents Introduction GitLab Continuous Integration (CI) Part GitLab Preparation […]<\/p>\n","protected":false},"author":89,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[6],"tags":[],"coauthors":[186],"class_list":["post-7410","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"yoast_head":"\nMLOps Pipeline with GitLab in Minutes - Comet<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/mlops-pipeline-with-gitlab-in-minutes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MLOps Pipeline with GitLab in Minutes\" \/>\n<meta property=\"og:description\" content=\"Photo by Sven Huls: https:\/\/www.pexels.com\/photo\/brown-wooden-bridge-over-green-trees-3801347\/ In this article, you\u2019ll learn deploy your Flask application to Heroku with GitLab in minutes! You\u2019ll implement the MLOps pipeline, with continuous integration (CI), and continuous deployment (CD), with easy-to-follow, step-by-step explanations. You\u2019ll automate the MLOps CI-CD pipeline with GitLab seamlessly. Contents Introduction GitLab Continuous Integration (CI) Part GitLab Preparation […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/mlops-pipeline-with-gitlab-in-minutes\/\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2023-09-11T17:12:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-24T17:14:18+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/miro.medium.com\/v2\/resize:fit:700\/1*7f_GLOM0ELXfOR_u7mJS1g.jpeg\" \/>\n<meta name=\"author\" content=\"Kaan Boke Ph.D.\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Kaan Boke Ph.D.\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"MLOps Pipeline with GitLab in Minutes - Comet","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.comet.com\/site\/blog\/mlops-pipeline-with-gitlab-in-minutes\/","og_locale":"en_US","og_type":"article","og_title":"MLOps Pipeline with GitLab in Minutes","og_description":"Photo by Sven Huls: https:\/\/www.pexels.com\/photo\/brown-wooden-bridge-over-green-trees-3801347\/ In this article, you\u2019ll learn deploy your Flask application to Heroku with GitLab in minutes! 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