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Failed to load latest commit information. View code. Terraform module for scalable self hosted GitHub action runners Motivation Overview Major configuration options. Terraform module for scalable self hosted GitHub action runners This Terraform module creates the required infrastructure needed to host GitHub Actions self-hosted, auto-scaling runners on AWS spot instances.

Motivation Overview Major configuration options. Overview The moment a GitHub action workflow requiring a self-hosted runner is triggered, GitHub will try to find a runner which can execute the workload. Select this option for ephemeral runners. When using the app option, the app needs to be installed to repo's are using the self-hosted runners.

a Webhook needs to be created. The webhook hook can be defined on enterprise, org, repo, or app level. The scale up lambda should have access to EC2 for creating and tagging instances.

The scale down lambda should have access to EC2 to terminate instances. Major configuration options. Org vs Repo level. You can configure the module to connect the runners in GitHub on an org level and share the runners in your org. Or set the runners on repo level and the module will install the runner to the repo. There can be multiple repos but runners are not shared between repos.

Checkrun vs Workflow job event. You can configure the webhook in GitHub to send checkrun or workflow job events to the webhook. Workflow job events are introduced by GitHub in September and are designed to support scalable runners. The OS and architecture are derived from the settings. By default the check is disabled. Linux vs Windows. you can configure the OS types linux and win.

Linux will be used by default. Re-use vs Ephemeral. By default runners are re-used for till detected idle. Once idle they will be removed from the pool. To improve security we are introducing ephemeral runners. Those runners are only used for one job. Ephemeral runners are only working in combination with the workflow job event. We also suggest using a pre-build AMI to improve the start time of jobs.

GitHub Cloud vs GitHub Enterprise Server GHES. The runner support GitHub Cloud as well GitHub Enterprise Server. For GHES we rely on our community to test and support. We have no possibility to test ourselves on GHES.

Spot vs on-demand. The runners use either the EC2 spot or on-demand life cycle. Runners will be created via the AWS CreateFleet API. The module scale up lambda will request via the CreateFleet API to create instances in one of the subnets and of the specified instance types.

Usages Examples are provided in the example directory. Terraform, or tfenv. Bash shell or compatible Docker optional, to build lambdas without node. AWS cli optional Node and yarn for lambda development. Setup GitHub App part 1 Go to GitHub and create a new app. Create app in Github Choose a name Choose a website mandatory, not required for the module.

Disable the webhook for now we will configure this later or create an alternative webhook. On the General page, make a note of the "App ID" and "Client ID" parameters. Generate a new private key and save the app. pem file. Setup terraform module Download lambdas To apply the terraform module, the compiled lambdas.

Service-linked role To create spot instances the AWSServiceRoleForEC2Spot role needs to be added to your account. com " }. terraform init terraform apply.

export interface GithubWorkflowEvent { workflowJobEvent: WorkflowJobEvent; }. About Terraform module for scalable GitHub action runners on AWS registry. github aws lambda scalable serverless terraform self-hosted cicd hacktoberfest github-actions actions-runner action-runner.

Releases 98 v1. Dec 9, You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. List of maps used to create the AMI filter for the action runner AMI.

By default amazon linux 2 is used. Optional SSM parameter that contains the runner AMI ID to launch instances from. The parameter value is managed outside of this module e. in a runner AMI build workflow.

This allows for AMI updates without having to re-apply this terraform config. The EC2 instance block device configuration. optional Replaces the module default cloudwatch log config. html for details. optional create the serviced linked role for spot instances that is required by the scale-up lambda.

The number of seconds the event accepted by the webhook is invisible on the queue before the scale up lambda will receive the event. Disable the auto update of the github runner agent. Be-aware there is a grace period of 30 days, see also the GitHub article.

Enabling the cloudwatch agent on the ec2 runner instances, the runner contains default config. Only scale if the job event received by the scale up lambda is is in the state queued. By default enabled for non ephemeral runners and disabled for ephemeral. Set this variable to overwrite the default behavior. Enabling the default managed security group creation.

Option to disable the lambda to sync GitHub runner distribution, useful when using a pre-build AMI. Should detailed monitoring be enabled for the runner. Set this to true if you want to use detailed monitoring. Enable to allow access the runner instances for debugging purposes via SSM. Note that this adds additional permissions to the runner instances. Should the userdata script be enabled for the runner. Set this to false if you are using your own prebuilt AMI. Enable a FIFO queue to remain the order of events received by the webhook.

Suggest to set to true for repo level runners. GitHub Enterprise SSL verification. Set to 'false' when custom certificate chains is used for GitHub Enterprise Server insecure. GitHub Enterprise Server URL. co - DO NOT SET IF USING PUBLIC GITHUB. GitHub app parameters, see your github app.

Ensure the key is the baseencoded. pem file the output of base64 app. Copyright © - Tencent Cloud. All Rights Reserved. 腾讯云 版权所有 京公网安备 粤B 腾讯云 备案 控制台. 开发者社区 学习. 搜索 关闭. 腾讯云 x Elasticsearch 携手三周年有奖征文大赛 参与即可获得联名精美周边礼品，更有Apple Watch、Cherry机械键盘、京东卡等大礼等你来拿～. 服务网格在腾讯 IT 的落地实践 云原生正发声第三十二期. 从爆红到被黑，游戏黑产攻防48小时 还原某爆款游戏的黑产对抗全过程. 腾讯云COS揽星招募令 参与活动可免费使用COS，更有多重劲爆福利等你拿！. 文章专区 知识问答 技术视频.

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It writes to all trace listeners which includes the VS output window when running in Debug mode. For a Console application. WriteLine would work but the output would still be generated in the release version of your binary.

Debug output should also appear in the normal output window when debugging tests; whereas, console. writeline output does not but can be found in the test output window. This requires a third party framework, namely Serilog , but I've nonetheless found it to be a very smooth experience with getting output to some place I can see it.

You first need to install Serilog's Trace sink. Once installed, you need to set up the logger like this:. You can set a different minimum level or set it to a config value or any of the normal Serilog functionality. You can also set the Trace logger to a specific level to override configs, or however you want to do this.

This doesn't seem like such a big deal, so let me explain some additional advantages. The biggest one for me was that I could simultaneously log to both the Output window and the console :. This gave me great flexibility in terms of how I consumed output, without having to duplicate all my calls to Console. Write with Debug. When writing the code, I could run my command line tool in Visual Studio without fear of losing my output when it exited.

When I had deployed it and needed to debug something and didn't have Visual Studio available , the console output was readily available for my consumption.

The same messages can also be logged to a file or any other kind of sink when it's running as a scheduled task. The bottom line is that using Serilog to do this made it really easy to dump messages to a multitude of destinations, ensuring I could always readily access the output regardless of how I ran it. This is not an answer to the original question. But since I found this question when searching for a means of interactively dumping object data, I figured others may benefit from mentioning this very useful alternative.

I ultimately used the Command Window and entered the Debug. Print command, as shown below. This printed a memory object in a format that can be copied as text, which is all I really needed.

Read the documentation for OutputDebugStringW here. Note that this method only works if you are debugging your code debug mode. Stack Overflow for Teams — Start collaborating and sharing organizational knowledge. Create a free Team Why Teams? Learn more about Collectives. Learn more about Teams. Writing to output window of Visual Studio Ask Question. Asked 10 years, 9 months ago. Modified 1 year, 7 months ago.

Viewed k times. Configuration: Active Debug Note: I created a project with the wizard as "Windows Forms Application" if relevant. c visual-studio visual-studio debugging. Improve this question. edited Jul 21, at Peter Mortensen asked Feb 27, at Since this is an older post, I'll add this as a comment for those who stumble across the question.

Instead of actually changing code, you can also use special breakpoints called tracepoints. See MSDN documentation — Wonko the Sane. Just a reminder that Debug. WriteLine will only work when running in Debug. That means running it with F5 and not CTRL-F5. This is easy to miss. If you are trying to write output from a unit test running under the Visual Studio test framework the rules are a little different, see this answer for details. Just to add on the comment kirk. burleson made; if you use Debug.

I would suggest Trace. Write as an alternative — Rob. Add a comment. Sorted by: Reset to default. Highest score default Trending recent votes count more Date modified newest first Date created oldest first. For more details, please refer to these: How to trace and debug in Visual C A Treatise on Using Debug and Trace classes, including Exception Handling. Improve this answer. edited Mar 20, at answered Feb 27, at Bhargav Bhargav 9, 1 1 gold badge 18 18 silver badges 29 29 bronze badges.

Thank you. I assume there is no way to write to output if i start without debugging ctrl-f5 right? I guess you're looking for this: stackoverflow.

Thanks again but that is not worked for me.

Work fast with our official CLI. Learn more. Please sign in to use Codespaces. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. There was a problem preparing your codespace, please try again. This Terraform module creates the required infrastructure needed to host GitHub Actions self-hosted, auto-scaling runners on AWS spot instances.

It provides the required logic to handle the life cycle for scaling up and down using a set of AWS Lambda functions. Runners are scaled down to zero to avoid costs when no workflows are active. And we are incredibly happy with all the feedback and contribution of the open-source community. In the next months we will speak at some conferences to share the solution and story of running this open-source project.

Via this questionaire we would like to gather feedback from the community to use in our talks. GitHub Actions self-hosted runners provide a flexible option to run CI workloads on the infrastructure of your choice. Currently, no option is provided to automate the creation and scaling of action runners. This module creates the AWS infrastructure to host action runners on spot instances.

It provides lambda modules to orchestrate the life cycle of the action runners. Lambda is chosen as the runtime for two major reasons. First, it allows the creation of small components with minimal access to AWS and GitHub. Secondly, it provides a scalable setup with minimal costs that works on repo level and scales to organization level. The main goal is to support Docker-based workloads. A logical question would be, why not Kubernetes?

In the current approach, we stay close to how the GitHub action runners are available today. The approach is to install the runner on a host where the required software is available. With this setup, we stay quite close to the current GitHub approach. Another logical choice would be AWS Auto Scaling groups. However, this choice would typically require much more permissions on instance level to GitHub.

And besides that, scaling up and down is not trivial. The moment a GitHub action workflow requiring a self-hosted runner is triggered, GitHub will try to find a runner which can execute the workload. The following options are available:. In AWS a API gateway endpoint is created that is able to receive the GitHub webhook events via HTTP post. The gateway triggers the webhook lambda which will verify the signature of the event. This check guarantees the event is sent by the GitHub App.

The accepted events are posted on a SQS queue. Messages on this queue will be delayed for a configurable amount of seconds default 30 seconds to give the available runners time to pick up this build. The "scale up runner" lambda listens to the SQS queue and picks up events. The lambda runs various checks to decide whether a new EC2 spot instance needs to be created.

For example, the instance is not created if the build is already started by an existing runner, or the maximum number of runners is reached. The Lambda first requests a registration token from GitHub, which is needed later by the runner to register itself. This avoids that the EC2 instance, which later in the process will install the agent, needs administration permissions to register the runner.

Next, the EC2 spot instance is created via the launch template. This script will install the required software and configure it. The registration token for the action runner is stored in the parameter store SSM , from which the user data script will fetch it and delete it once it has been retrieved. Once the user data script is finished, the action runner should be online, and the workflow will start in seconds.

Scaling down the runners is at the moment brute-forced, every configurable amount of minutes a lambda will check every runner instance if it is busy. In case the runner is not busy it will be removed from GitHub and the instance terminated in AWS. At the moment there seems no other option to scale down more smoothly.

Downloading the GitHub Action Runner distribution can be occasionally slow more than 10 minutes. Therefore a lambda is introduced that synchronizes the action runner binary from GitHub to an S3 bucket. The EC2 instance will fetch the distribution from the S3 bucket instead of the internet.

Secrets and private keys are stored in SSM Parameter Store. These values are encrypted using the default KMS key for SSM or passing in a custom KMS key. Permission are managed on several places. Below the most important ones.

For details check the Terraform sources. Besides these permissions, the lambdas also need permission to CloudWatch for logging and scheduling , SSM and S3. For more details about the required permissions see the documentation of the IAM module which uses permission boundaries. To be able to support a number of use-cases the module has quite a lot of configuration options.

We try to choose reasonable defaults. The several examples also show for the main cases how to configure the runners. See below for more details. Examples are provided in the example directory. Please ensure you have installed the following tools. The module supports two main scenarios for creating runners.

On repository level a runner will be dedicated to only one repository, no other repository can use the runner.

On organization level you can use the runner s for all the repositories within the organization. See GitHub self-hosted runner instructions for more information. Before starting the deployment you have to choose one option.

The setup consists of running Terraform to create all AWS resources and manually configuring the GitHub App. The Terraform module requires configuration from the GitHub App and the GitHub app requires output from Terraform.

Therefore you first create the GitHub App and configure the basics, then run Terraform, and afterwards finalize the configuration of the GitHub App. Go to GitHub and create a new app. Beware you can create apps for your organization or for a user. For now we support only organization level apps. To apply the terraform module, the compiled lambdas.

zip files need to be available either locally or in an S3 bucket. They can be either downloaded from the GitHub release page or build locally. The lambdas can be downloaded manually from the release page or using the download-lambda terraform module requires curl to be installed on your machine. The lambdas will be saved to the same directory. For local development you can build all the lambdas at once using. sh or individually using yarn dist. To create spot instances the AWSServiceRoleForEC2Spot role needs to be added to your account.

You can do that manually by following the AWS docs. Be aware this is an account global role, so maybe you don't want to manage it via a specific deployment. Next create a second terraform workspace and initiate the module, or adapt one of the examples. pem file i. the output of base64 app. The terraform output displays the API gateway url endpoint and secret, which you need in the next step. The lambda for syncing the GitHub distribution to S3 is triggered via CloudWatch by default once per hour.

After deployment the function is triggered via S3 to ensure the distribution is cached. At this point you have 2 options. Either create a separate webhook enterprise, org, or repo , or create webhook in the App. The module support 2 scenarios to manage environment secrets and private key of the Lambda functions.

You have to create and configure you KMS key. The module will use the context with key: Environment and value var. environment as encryption context.

The module basically supports two options for keeping a pool of runners. One is via a pool which only supports org-level runners, the second option is keeping runners idle. The pool is introduced in combination with the ephemeral runners and is primary meant to ensure if any event is unexpected dropped, and no runner was created the pool can pick up the job. The pool is maintained by a lambda.

WriteLine to quickly print a message to the output window of the IDE. You can configure runners to be ephemeral, runners will be used only for one job. I assume there is no way to write to output if i start without debugging ctrl-f5 right? answered Feb 27, at And we are incredibly happy with all the feedback and contribution of the open-source community. You can harden the instance by providing your own AMI and overwriting the cloud-init script.

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