[{"data":1,"prerenderedAt":774},["ShallowReactive",2],{"/en-us/blog/100-runners-in-less-than-10mins-and-less-than-10-clicks":3,"navigation-en-us":39,"banner-en-us":467,"footer-en-us":484,"Darwin Sanoy-Nupur Sharma":728,"footer-source-/en-us/blog/100-runners-in-less-than-10mins-and-less-than-10-clicks/":753,"next-steps-en-us":759},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":29,"_id":32,"_type":33,"title":34,"_source":35,"_file":36,"_stem":37,"_extension":38},"/en-us/blog/100-runners-in-less-than-10mins-and-less-than-10-clicks","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"Setting up 100 AWS Graviton Spot Runners for GitLab","Utilizing the GitLab HA Scaling Runner Vending Machine for AWS Automation to setup 100 GitLab runners on AWS Spot.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669882/Blog/Hero%20Images/hundredgitlabspotrunner.png","https://about.gitlab.com/blog/100-runners-in-less-than-10mins-and-less-than-10-clicks","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"How to provision 100 AWS Graviton GitLab Spot Runners in 10 Minutes for $2/hour\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"Darwin Sanoy\"},{\"@type\":\"Person\",\"name\":\"Nupur Sharma\"}],\n        \"datePublished\": \"2021-08-17\",\n      }",{"title":17,"description":10,"authors":18,"heroImage":11,"date":21,"body":22,"category":23,"tags":24},"How to provision 100 AWS Graviton GitLab Spot Runners in 10 Minutes for $2/hour",[19,20],"Darwin Sanoy","Nupur Sharma","2021-08-17","Managing elastically scaled or highly available compute infrastructures is\none of the key challenges the cloud was built for. Application scaling\nconcerns can be handled by cloud services that are purpose designed,\nrigorously tested, and continually improved. This article dives into some\nspecific enablement automation that brings the benefits of AWS Autoscaling\nGroups (ASG) to runner management. There are benefits to both the largest\nfleets and single instance runners.\n\n\nEmbedded in this article is a YouTube video that demonstrates the deployment\nof 100 GitLab runners on Amazon EC2 Spot compute in less than 10 minutes\nusing less than 10 clicks. The video also shows updating this entire fleet\nin under 10 minutes to emphasize the time savings of built-in maintenace.\n\n\nThe information and automation in this article applies to GitLab Private\nRunners which are deployed on your own compute resources. Self-managed\nGitLab instances require private runners, but they can also be configured\nand used with GitLab.com SaaS accounts.\n\n\n## Well-architected runner management\n\n\nThere are many different reasons that a customer might need to deploy\nmultiple runners with various characteristics. Some of the more popular ones\nare:\n\n\n- Workloads that require large-scale runner fleets.\n\n- To gain cost savings through Spot compute, uptime scheduling, and ARM\narchitecture.\n\n- Projects with high demand of CI activity to make sure that the runner is\nnot being held up by jobs on another project.\n\n- Jobs that have special security requirements, e.g., security credentials,\nrole-based access or managed identities for Continuous Delivery (CD). These\nsecurity requirements can enable instance-level (AWS IAM Instance Profile)\nsecurity by allowing runners with sufficient rights to deploy in specific\ntarget environments. For example, a CD runner for non-production\nenvironments and a different runner for production.\n\n- Implementing role-based access control rather than user-based. This means\nusers don't have to use secrets to manage security requirements for CI jobs\nto accomplish their tasks.\n\n- Development teams can be confident the runner has the same capabilities\nfor CI and CD automation they test through their interactive logins by\nleveraging a common IAM role.\n\n\n### The challenges of building production-grade elastic GitLab Runners\n\n\n[The GitLab Runner](https://docs.gitlab.com/runner/) is the workhorse of\nGitLab CI and CD capabilities. The runner can handle numerous operating\nenvironments and automation functions for a GitLab instance. The GitLab\nRunner has become very sophisticated due to the broad range of supported\nenvironments. In order to successfully configure the GitLab Runner as a\nset-it-and-forget-it service, the user has to work through many different\ndecisions and considerations. We summarize some of the GitLab\nRunner-specific considerations that can be challenging:\n\n\n- There are a lot of configuration options and scenarios to sort through. It\ncan be an iterative process to discover what needs to be done to set up\nGitLab Runners.\n\n- Ensuring runners are a production-grade capability requires Infrastructure\nas Code (IaC) development so that high availability and scaling can be\nachieved by automatically spawning new instances.\n\n- Ensuring that runner deregistration happens correctly when GitLab Runners\nare automatically scaled in.\n\n- Additional cost-saving configurations, such as Spot compute and scheduled\nrunner uptime, can complicate the automation requirements for AWS\nAutoscaling Groups (ASGs).\n\n- Large organizations often want developers to be able to easily\nself-service deploy runners with various configurations. Service Management\nAutomation (SMA) has been made popular with products like Service Now, AWS\nService Catalog, and AWS Control Tower. This automation is compatible with\nSMA.\n\n- It can be difficult to map runners to AWS and map AWS to runners in large\norganizations with numerous runners and AWS accounts.\n\n\n### Introducing the GitLab HA Scaling Runner Vending Machine for AWS\n\n\nAn effective way to handle multiple design considerations is to make a\nreusable tool. To help you with best practice runner deployments on AWS, we\ncreated the [GitLab HA Scaling Runner Vending Machine for\nAWS](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/)\n(\"The GitLab Runner Vending Machine\"). It is created in AWS’ Infrastructure\nas Code, known as CloudFormation.\n\n\n> **Designed with AWS Well Architected:** This automation has many features\nbeyond the scope of this blog post. The primary focus of this blog post is\non managing costs. See the [full list of features\nhere](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/-/blob/main/FEATURES.md).\n\n\nThe GitLab Runner Vending Machine has the following cost management and\nscaling management benefits, exposed as a variety of parameters:\n\n\n- The ability to leverage Spot compute instances. This is important because\nit leaves CI/CD pipeline developers in charge of whether specific Gitlab\nCI/CD jobs run on Spot compute or not.\n\n- ASG-scheduled scaling so that a runner or runner fleet can be completely\nshutdown when not in use.\n\n- The GitLab Runner Vending Machine can leverage ARM compute for Linux -\nwhich runs faster and costs less.\n\n- It can also use ASG to update all runners in a fleet with the latest\nmachine images and GitLab Runner version (or a specific version). When\nmaintenance is not built-in, the labor cost of keeping things up-to-date can\nbe significant.\n\n- Runner naming and tagging in AWS and GitLab, which eases the burden of\nlocating runner instances and managing orphaned runners registrations,\nwhether it is manual or automated.\n\n\n### How to save money with The GitLab Runner Vending Machine\n\n\nSignificant savings are possible with this IaC, whether your team wants to\nsave on a single runner or a fleet of them.\n\n\nThe savings calculations below are for a single runner and should be linear\nfor a given workload. To calculate your savings for more runners, simply\nmultiply the final result by the number of runner instances. The available\n\"Runner Minutes\" per hour is calculated as the runner's job concurrency\nsetting multiplied by the minutes in an hour. For this exercise, we'll use\njob concurrency of \"10\". This number should be changed depending on the\ninstance types you are using and the load testing of your typical CI/CD\nworkloads.\n\n\nJust like most performance analysis, we are assuming that hardware resource\nutilization is optimal and consistent. If a runner cluster can sustain\nrespectable performance with 80% CPU loading, this calculation assumes that\nwould be maintained regardless of the size of the cluster.\n\n\n#### AWS Graviton ARM and Spot savings\n\n\nThe GitLab Runner engineering team has completed performance testing that\ndemonstrates performance gains of more than 30% on some AWS Graviton\n(ARM-based) instance types. Assuming that runners are performance-managed\nfor optimized utilization, this gain is a direct cost savings. Just\nrecently, we shared [how deploying GitLab on Arm-based AWS Graviton2\nresulted in cost savings of 23% and 36% performance\ngains](/blog/achieving-23-cost-savings-and-36-performance-gain-using-gitlab-and-gitlab-runner-on-arm-neoverse-based-aws-graviton2-processor/).\n\n\n![ARM Efficiency Test Results For GitLab\nRunner](https://about.gitlab.com/images/blogimages/hundred-runners/hundredrunners-image1.png)\n\nGitLab Runner testing results for ARM-efficiency gains.\n\n{: .note.text-center}\n\n\n#### Scheduling savings\n\n\nThe savings can be dramatic when teams are able to turn off runners when not\nin use. For instance: Scheduling a runner to operate for 40-hours per week\nsaves 76% when compared to the cost of running it for 168 hours. Runners\nthat are just in use for 10 hours per week saves 94%.\n\n\n#### Combining scheduling, Spot, and ARM to save 97%\n\n\nJust for fun, let's see what savings are possible by comparing a standard\nrunner scenario with deploying runners in customized, stand-alone instances\nto the maximum savings automation can deliver.\n\n\nImagine I am a developer who set up a custom GitLab Runner on an m5.xlarge\ninstance, which is x86 the architecture, for a development team that works\nfor 40 hours on the same time zone. Since there is no automation, the GitLab\nRunner runs 24/7. We will assume a job concurrency of 10, which gives 600\n\"runner minutes\" per hour of run time. Scheduling uptime, running on Spot,\nand leveraging ARM can all be achieved quickly by redeploying the runner\nwith The GitLab Runner Vending Machine.\n\n\nHere is the calculation to run the configuration described above, for one\nweek: On Demand, x86, Always On: 1 x m5.xlarge = .192/hr x 168 hrs/week =\n**$32/week or $1664/year**\n\n\nHere are the savings that come from running Spot, ARM, and scheduling the\nRunner to be up just 40hrs/week: 1 x m6g.large Spot = .0419 x 40hrs/week x\n64% (36% better performance) = **$1/week**\n\n\n$1/$32 x 100 = 3.125% of the original cost for the same work. In other\nwords, **we just saved 97%** without ever impacting the ability to get the\njob done.\n\n\nIn short, The GitLab Runner Vending Machine intends to bring the many cost\nsaving mechanisms of AWS Cloud computing to your GitLab Runner fleets.\n\n\nYou can save costs by using ARM/Graviton instances, Spot compute, or by\nscheduling uptime. In many cases, you can combine all three savings\nmechanisms for maximum impact.\n\n\n### Special pipeline building concerns for Spot Runners\n\n\nSpot instances can disappear with as little as two minutes of warning. This\ninevitably means some runners will be terminated while jobs are still in\nprogress. CI/CD pipeline developers must take into account whether a job\nought to run on compute resources that can disappear with short notice (so\nshort as to be considered \"no notice\"). This comes down to deciding what\njobs are OK to run on Spot and what jobs should instead run on AWS'\npersistent compute known as \"On-Demand\".\n\n\nThe GitLab Runner Vending Machine accounts for these constraints by tagging\nrunner instances in GitLab with `computetype-spot` or `computetype-ondemand`\n– indicating in the \"tags\" segment of GitLab CI/CD jobs if a job should run\non Spot compute.\n\n\nSome types of CI workloads, e.g., mass performance testing or large unit\ntesting suites, may already have work queues and work tracking that make it\nideal for Spot compute. Other activities, e.g., polling another system for a\ndeployment status, could suffer a material discrepancy if terminated\npermaturely. Others, such as building the application, are sort of in the\nmiddle. Usually, restarting the build is sufficient.\n\n\n### Job configuration for Spot\n\n\nIf you need to reschedule terminated work, it is helpful to configure\nGitLab’s job `retry:` keyword. When working with a dispatching engine or\nwork queue that automatically accounts for incompleted work by processing\nagents, the retry configuration is unnecessary.\n\n\nHere is an example that implements both of these concepts:\n\n\n```\n\nmy-scaled-test-suite:\n  parallel: 100\n  tags:\n  - computetype-Spot\n  retry:\n    max: 2\n    when:\n      - runner_system_failure\n      - unknown_failure\n```\n\n\nThe usage and limitations of `retry:` are discussed in greater detail in the\n[GitLab CI documentation on\nretry](https://docs.gitlab.com/ee/ci/yaml/#retry).\n\n\n### How to get started\n\n\nThe CloudFormation templates for the [GitLab Runner Vending Machine are\nmanaged in a public project on\nGitLab.com](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/).\nThere is a lot of information in the project about how the solution works\nand what problems it aims to solve, and will be useful for very experienced\nAWS builders.\n\n\nBut to keep it simple for users who want the quickest path to creating\nrunners of all sizes, it also has an \"easy button\" page that has a table\nthat looks like this:\n\n\n![Easy Button Page\nSample](https://about.gitlab.com/images/blogimages/hundred-runners/hundredrunners-image2.png)\n\nThe easy buttons launch a CloudFormation Quick Create that only requires\nfilling in a few fields.\n\n{: .note.text-center}\n\n\nKeep in mind that easy buttons intentionally hide the high degree of\ncustomization that is possible with this automation by setting the\nparameters for the most common scenarios in advance. Advanced AWS users\nshould read more of the documentation in the repository to understand that\nthe GitLab Runner Vending Machine is also capable of creating sophisticated\nrunner fleets.\n\n\nFirst, click the CloudFormation icons to launch the Easy Button template\ndirectly into the CloudFormation Quick Create console. The Quick Create\nconsole is designed for simplicity to enable you to complete the prompts and\nthen click one button to launch the stack.\n\n\n![CloudFormation Quick Create\nExample](https://about.gitlab.com/images/blogimages/hundred-runners/hundredrunners-image3.png){:\n.shadow.medium.center}\n\nThis is a typical Quick Create form for the GitLab Vending Machine easy\nbuttons.\n\n{: .note.text-center}\n\n\nNext, select the deploy region by using the drop down menu in the upper\nright of the console (where the screenshot says \"Oregon\").\n\n\nIn most cases, you will only need to add your GitLab instance URL\n(GitLab.com is fine if that is where your repositories are), and the runner\ntoken, which you retrieve from the group level or project you wish to attach\nthe runners to. If you are registering against a self-managed instance, you\ncan use the instance-level tokens from the administrator console to register\nthe runner for use across the entire instance. Read on for [instructions for\nfinding Runner Registration\nTokens](https://docs.gitlab.com/runner/register/#requirements).\n\n\nA few other customization parameters are available for your convenience.\n\n\nNote that the automation attempts to use the default VPC of the region in\nwhich you deploy and the default security group for the VPC. In some\norganizations, default VPCs and/or their security groups are locked. You can\ndeploy to custom VPCs by using the full template instead of an easy button.\nOn the easy button page look for the footnote \"Not any easy button person?\"\"\nto find a link to the full template.\n\n\nWatch the video below to see the deployment of provisioning 100 GitLab Spot\nRunners on AWS in less than 10 minutes and in less than 10 clicks for just\n$5 per hour.\n\n\n\u003C!-- blank line -->\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/EW4RJv5zW4U\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\n\u003C!-- blank line -->\n\n\nCheck out the YouTube playlist for more relevant videos about [GitLab and\nAWS](https://youtube.com/playlist?list=PL05JrBw4t0Ko30Bkf8bAvR-8E441Fy2G9)\n\n\n### This automation does much, much more\n\n\nWhile this article focused how much you can saving while using Spot for\nscaled runners, the underlying automation is capable of many other\nscenarios. Below is a summary of the additional features and benefits\ncovered in the documentation.\n\n\n- Scaled runners that are persistent (not Spot) ([see more easy buttons\nhere](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/-/blob/main/easybuttons.md)).\n\n- Supports small, single runner setups and scaled ones.\n\n- Supports GitLab.com SaaS or self-managed instances.\n\n- Automates OS patching and Runner version upgrading.\n\n- Supports Windows and Linux.\n\n- Can be reused with Amazon provisioning services such as Service Catalog\nand Control Tower.\n\n- Implements least privilege security throughout.\n\n- Supports deregistering runners on scale-in or Spot termination.\n\n\nA full feature list is in the document [Features of GitLab HA Scaling Runner\nVending Machine for\nAWS](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/-/blob/main/FEATURES.md)\n\n\n### Easy running\n\n\nWe hope that this automation will make deployment of runners of all sizes\nsimple for you. 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