[{"data":1,"prerenderedAt":720},["ShallowReactive",2],{"/en-us/blog/there-is-no-mlops-without-devsecops/":3,"navigation-en-us":37,"banner-en-us":466,"footer-en-us":483,"William Arias":692,"next-steps-en-us":705},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":27,"_id":30,"_type":31,"title":32,"_source":33,"_file":34,"_stem":35,"_extension":36},"/en-us/blog/there-is-no-mlops-without-devsecops","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"Building GitLab with GitLab: Why there is no MLOps without DevSecOps","Follow along as data scientists adopt DevSecOps practices and enjoy the benefits of automation, repeatable workflows, standardization, and automatic provisioning of infrastructure.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659740/Blog/Hero%20Images/building-gitlab-with-gitlab-no-type.png","https://about.gitlab.com/blog/there-is-no-mlops-without-devsecops","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"Building GitLab with GitLab: Why there is no MLOps without DevSecOps\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"William Arias\"}],\n        \"datePublished\": \"2023-10-05\",\n      }",{"title":9,"description":10,"authors":17,"heroImage":11,"date":19,"body":20,"category":21,"tags":22},[18],"William Arias","2023-10-05","Building predictive models requires a good amount of experimentation and\niterations. Data scientists building those models usually implement\nworkflows involving several steps such as data loading, processing,\ntraining, testing, and deployment. Such workflows or data science pipelines\ncome with a set of challenges on their own; some of these common challenges\nare:\n\n- prone to error due to manual steps\n\n- experimentation results that are hard to replicate\n\n- long training time of machine learning (ML) models \n\n\nWhen there is a challenge, there is also an opportunity; in this case, those\nchallenges represent an opportunity for data scientists to adopt DevSecOps\npractices and enjoy the benefits of automation, repeatable workflows,\nstandardization, and automatic provisioning of infrastructure needed for\ndata-driven applications at scale.\n\n\nThe [Data Science team at\nGitLab](https://about.gitlab.com/handbook/business-technology/data-team/organization/data-science/)\nis now utilizing the GitLab DevSecOps Platform in their workflows,\nspecifically to:\n\n- enhance experiment reproducibility by ensuring code and data execute in a\nstandardized container image\n\n- automate training and re-training of ML models with GPU-enabled CI/CD\n\n- leverage ML experiment tracking, storing the most relevant metadata and\nartifacts produced by data science pipelines automated with CI\n\n\nAt GitLab, we are proponents of \"dogfooding\" our platform and sharing how we\nuse GitLab to build GitLab. What follows is a detailed look at the Data\nScience team's experience.\n\n\n### Enhancing experiment reproducibility \n\nA baseline step to enhance reproducibility is having a common and standard\nexperiment environment for all data scientists to run experiments in their\nJupyter Notebooks. A standard data science environment ensures that all team\nmembers use the same software dependencies. A way to achieve this is by\nbuilding a container image with all the respective dependencies under\nversion control and re-pulling it every time a new version of the code is\nrun. This process is illustrated in the figure below:\n\n\n![build](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/build-2.png)\n\nData science image of automatic build using GitLab CI \n\n{: .note.text-center}\n\n\nYou might wonder if the image gets built every time there is a new commit.\nThe answer is \"no\" since that would result in longer execution times, and\nthe image dependencies versions don’t change frequently, rendering it\nunnecessary to build it every time there is a new commit. Therefore, once\nthe standard image is automatically built by the pipeline, it is pushed to\nthe GitLab Container Registry, where it is stored and ready to be pulled\nevery time changes to the model code are introduced, and re-training is\nnecessary.\n\n\n![registry](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/registry.png)\n\nGitLab Container Registry with image automatically built and pushed by a CI\npipeline\n\n{: .note.text-center}\n\n\nChanges to the image dependencies or Dockerfile require a [merge\nrequest](https://docs.gitlab.com/ee/user/project/merge_requests/) and an\napproval process.\n\n\n### How to build the data science image using GitLab CI/CD\n\nConsider this project structure:\n\n\n```\n\nnotebooks/\n\n.gitlab-ci.yml\n\nDockerfile\n\nconfig.yml\n\nrequirements.txt\n\n```\n\nGitLab's Data Science team already had a pre-configured JupyterLab image\nwith packages such as [gitlabds](https://pypi.org/project/gitlabds/1.0.0/)\nfor common data preparation tasks and modules to enable Snowflake\nconnectivity for loading raw data. All these dependencies are reflected in\nthe Dockerfile at the root of the project, plus all the steps necessary to\nbuild the image: \n\n\n```\n\nFROM nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04\n\nCOPY .    /app/\n\nWORKDIR /app\n\nRUN apt-get update\n\nRUN apt-get install -y python3.9\n\nRUN apt-get install -y python3-pip\n\nRUN pip install -r requirements.txt\n\n```\n\n\nThe instructions to build the data science image start with using Ubuntu\nwith CUDA drivers as a base image. We are using this baseline image because,\nmoving forward, we will use GPU hardware to train models. The rest of the\nsteps include installing Python 3.9 and the dependencies listed in\n`requirements.txt` with their respective versions. \n\n\nAutomatically building the data science image using [GitLab\nCI/CD](https://about.gitlab.com/topics/ci-cd/) requires us to create the\n`.gitlab-ci.yml ` at the root of the project and use it to describe the jobs\nwe want to automate. For the time being, let’s focus only on the\n`build-ds-image`job:\n\n\n```\n\nvariables:\n  DOCKER_HOST: tcp://docker:2375\n  MOUNT_POINT: \"/builds/$CI_PROJECT_PATH/mnt\"\n  CONTAINER_IMAGE: \"$CI_REGISTRY_IMAGE/main-image:latest\"\n\nstages:\n    - build\n    - train\n    - notify\ninclude:\n  - template: 'Workflows/MergeRequest-Pipelines.gitlab-ci.yml'\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n\nbuild-ds-image:\n  tags: [ saas-linux-large-amd64 ]\n  stage: build\n  services:\n    - docker:20.10.16-dind\n  image:\n    name: docker:20.10.16\n  script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - docker build -t $CONTAINER_IMAGE .\n    - docker push $CONTAINER_IMAGE\n  rules:\n    - if: '$CI_PIPELINE_SOURCE == \"merge_request_event\" && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME == $CI_DEFAULT_BRANCH'\n      changes:\n       - Dockerfile\n       - requirements.txt\n\n  allow_failure: true\n```\n\n\nAt a high level, the job `build-ds-image`:\n\n- uses a docker-in-docker service (dind) necessary to create docker images\nin GitLab CI/CD.\n\n- uses [predefined variables](link) to log into the GitLab Container\nRegistry, build the image, tag it using $CONTAINER_IMAGE variable, and push\nit to the registry. These steps are declared in the script section lines.\n\n- leverages a  `rules` section to evaluate conditions to determine if the\njob should be created. In this case, this job runs only if there are changes\nto the Dockerfile and requirements.txt file and if those changes are created\nusing a merge request.\n\n\nThe conditions declared in `rules` helps us optimize the pipeline running\ntime since the image gets rebuilt only when necessary.\n\n\nA complete pipeline can be found in this example project, along with\ninstructions to trigger the automatic creation of the data science image:\n[Data Science CI\npipeline](https://gitlab.com/gitlab-data/data-science-ci-example/-/blob/main/.gitlab-ci.yml?ref_type=heads).\n\n\n### Automate training and re-training of ML models with GPU-enabled CI/CD\n\nGitLab offers the ability to leverage GPU hardware and, even better, to get\nthis hardware automatically provisioned to run jobs declared in the\n.gitlab-ci.yml file. We took advantage of this capability to train our ML\nmodels faster without spending time setting up or configuring graphics card\ndrivers. Using GPU hardware ([GitLab\nRunners](https://docs.gitlab.com/ee/ci/runners/saas/gpu_saas_runner.html))\nrequires us to add this line to the training job: \n\n\n```\n\ntags:\n        - saas-linux-medium-amd64-gpu-standard\n```\n\n\nThe tag above will ensure that a GPU GitLab Runner automatically picks up\nevery training job.\n\nLet’s take a look at the entire training job in the .gitlab-ci.yml file and\nbreak down what it does:\n\n\n```\n\ntrain-commit-activated:\n    stage: train\n    image: $CONTAINER_IMAGE\n    tags:\n        - saas-linux-medium-amd64-gpu-standard\n    script:\n        - echo \"GPU training activated by commit message\"\n        - echo \"message passed is $CI_COMMIT_MESSAGE\"\n        - notebookName=$(echo ${CI_COMMIT_MESSAGE/train})\n        - echo \"Notebook name $notebookName\"\n        - papermill -p is_local_development False -p tree_method 'gpu_hist' $notebookName -\n    rules:\n        - if: '$CI_COMMIT_BRANCH == \"staging\"'\n          when: never\n        - if: $CI_COMMIT_MESSAGE =~ /\\w+\\.ipynb/\n          when: always\n          allow_failure: true\n    artifacts:\n      paths:\n        - ./model_metrics.md\n````\n\n\nLet’s start with this block:\n\n\n```\n\ntrain-commit-activated:\n    stage: train\n    image: $CONTAINER_IMAGE\n    tags:\n        - saas-linux-medium-amd64-gpu-standard\n```\n\n\n- **train-commit-activated** This is the name of the job. Since the model\ntraining gets activated given a specific pattern in the commit message, we\nuse a descriptive name to easily identify it in the larger pipeline.\n\n- **stage: train** This specifies the pipeline stage where this job belongs.\nIn the first part of the CI/CD configuration, we defined three stages for\nthis pipeline: `build`, `train`,  and `notify`. This job comes after\nbuilding the data science container image. The order is essential since we\nfirst need the image built to run our training code in it.\n\n- **image: $CONTAINER_IMAGE** Here, we specify the Docker image built in the\nfirst job that contains the CUDA drivers and necessary Python dependencies\nto run this job. $CONTAINER_IMAGE is a user-defined variable specified in\nthe variables section of the .gitlab-ci.yml file. \n\n- **tags: saas-linux-medium-amd64-gpu-standard** As mentioned earlier, using\nthis line, we ask GitLab to automatically provision a GPU-enabled Runner to\nexecute this job.\n\n\nThe second block of the job:\n\n\n```\n\nscript:\n        - echo \"GPU training activated by commit message\"\n        - echo \"message passed is $CI_COMMIT_MESSAGE\"\n        - notebookName=$(echo ${CI_COMMIT_MESSAGE/train})\n        - echo \"Notebook name $notebookName\"\n        - papermill -p is_local_development False -p tree_method 'gpu_hist' $notebookName -\n```\n\n\n- **script** This section contains the commands in charge of running the\nmodel training. The execution of this job is conditioned to the contents of\nthe  commit message. The commit message must have the name of the Jupyter\nNotebook that contains the actual model training code.\n\n\nThe rationale behind this approach is that we wanted to keep the data\nscientist workflow as simple as possible. The team had already adopted the\n[modeling\ntemplates](https://gitlab.com/gitlab-data/data-science/-/tree/main/templates)\nto start building predictive models quickly. Plugging the CI pipeline into\ntheir modeling workflow was a priority to ensure productivity would remain\nintact. With these steps:\n\n\n```\n\nnotebookName=$(echo ${CI_COMMIT_MESSAGE/train})\n        - echo \"Notebook name $notebookName\"\n        - papermill -p is_local_development False -p tree_method 'gpu_hist' $notebookName -\n```\n\n\nThe CI pipeline captures the name of the Jupyter Notebook with the training\nmodeling template and passes parameters to ensure\n[XGBoost](https://xgboost.readthedocs.io/en/stable/) uses the provisioned\nGPU. You can find an example of the Jupyter modeling template that is\nexecuted in this job\n[here](https://gitlab.com/gitlab-data/data-science-ci-example/-/blob/main/notebooks/training_example.ipynb?ref_type=heads).\n\n\nOnce the data science image is built, it can be reutilized in further model\ntraining jobs. The `train-commit-activated` job pulls the image from the\nGitLab Container Registry and utilizes it to run the ML pipeline defined in\nthe training notebook. This is illustrated in the `CI Job - Train model` in\nthe figure below:\n\n\n![training](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/training_job.png)\n\nTraining job executes ML pipeline defined in the modeling notebook\n\n{: .note.text-center}\n\n\nSince our image contains CUDA drivers and GitLab automatically provisions\nGPU-enabled hardware, the training job runs significantly faster with\nrespect to standard hardware.\n\n\n### Using GitLab ML experiment tracker\n\nEach model training execution triggered using GitLab CI is an experiment\nthat needs tracking. Using Experiment tracking in GitLab helps us to record\nmetadata that comes in handy to compare model performance and collaborate\nwith other data scientists by making result experiments available for\neveryone and providing a detailed history of the model development.\n\n\n![experiments](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/experiments.png)\n\nExperiments automatically logged on every CI pipeline GPU training run \n\n{: .note.text-center}\n\n\nEach model artifact created can be traced back to the pipeline that\ngenerated it, along with its dependencies:\n\n\n![traceability](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/traceability_small.png)\n\nModel traceability from pipeline run to candidate details\n\n{: .note.text-center}\n\n\n### Putting it all together\n\nWhat is machine learning without data to learn from? We also leveraged the\n[Snowflake](https://www.snowflake.com/en/) connector in the model training\nnotebook and automated the data extraction whenever the respective commit\ntriggers a training job. Here is an architecture of the current solution\nwith all the parts described in this blog post:\n\n\n![process](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/training_fixed.png)\n\nData Science pipelines automated using GitLab DevSecops Platform\n\n{: .note.text-center}\n\n\n| Challenge | Solution |\n\n| ------ | ------ | \n\n|Prone to error due to manual steps | Automate steps with [GitLab\nCI/CD](https://docs.gitlab.com/ee/ci/) |\n\n|Experimentation results that are hard to replicate    |  Record metadata\nand model artifacts with [GitLab Experiment\nTracker](https://docs.gitlab.com/ee/user/project/ml/experiment_tracking/)   \n|\n\n|The long training time of machine learning models     |  Train models with\n[GitLab SaaS GPU\nRunners](https://docs.gitlab.com/ee/ci/runners/saas/gpu_saas_runner.html)  |\n\n\nIterating on these challenges is a first step towards MLOps, and we are at\nthe tip of the iceberg; in coming iterations, we will adopt security\nfeatures to ensure model provenance (software bill of materials) and code\nquality, and to monitor our ML workflow development with value stream\ndashboards. But so far, one thing is sure: **There is no MLOps without\nDevSecOps**.\n\n\nGet started automating your data science pipelines, follow this\n[tutorial](https://about.gitlab.com/handbook/business-technology/data-team/platform/ci-for-ds-pipelines/)\nand clone this\n[data-science-project](https://gitlab.com/gitlab-data/data-science-ci-example)\nto follow along and watch this demo of using GPU Runners to train\n[XGBoost](https://xgboost.readthedocs.io/en/stable/) model.\n\n\nSee how data scientists can train ML models with GitLab GPU-enabled Runners\n(XGBoost 5-minute demo):\n\n\n\u003C!-- blank line -->\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/tElegG4NCZ0?si=L1IZfx_UGv6u81Gk\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\n\u003C!-- blank line -->\n\n\n## More \"Building GitLab with GitLab\" blogs\n\nRead more of our \"Building GitLab with GitLab\" series:\n\n- [How we use Web API fuzz\ntesting](https://about.gitlab.com/blog/building-gitlab-with-gitlab-api-fuzzing-workflow/)\n\n- [How GitLab.com inspired GitLab\nDedicated](https://about.gitlab.com/blog/building-gitlab-with-gitlabcom-how-gitlab-inspired-dedicated/)\n","ai-ml",[23,24,25,26],"tutorial","DevSecOps","DevSecOps platform","AI/ML",{"slug":28,"featured":6,"template":29},"there-is-no-mlops-without-devsecops","BlogPost","content:en-us:blog:there-is-no-mlops-without-devsecops.yml","yaml","There Is No Mlops Without 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