Autoscaling GitLab Runner on AWS EC2 All tiers All offerings
One of the biggest advantages of GitLab Runner is its ability to automatically spin up and down VMs to make sure your builds get processed immediately. It’s a great feature, and if used correctly, it can be extremely useful in situations where you don’t use your runners 24/7 and want to have a cost-effective and scalable solution.
Introduction
In this tutorial, we’ll explore how to properly configure GitLab Runner in AWS. The instance in AWS will serve as a runner manager that spawns new Docker instances on demand. The runners on these instances are automatically created. They use the parameters covered in this guide and do not require manual configuration after creation.
In addition, we’ll make use of Amazon’s EC2 Spot instances which will greatly reduce the costs of the GitLab Runner instances while still using quite powerful autoscaling machines.
Prerequisites
A familiarity with Amazon Web Services (AWS) is required as this is where most of the configuration will take place.
We suggest a quick read through Docker machine
amazonec2
driver documentation
to familiarize yourself with the parameters we will set later in this article.
Your GitLab Runner is going to need to talk to your GitLab instance over the network, and that is something you need think about when configuring any AWS security groups or when setting up your DNS configuration.
For example, you can keep the EC2 resources segmented away from public traffic in a different VPC to better strengthen your network security. Your environment is likely different, so consider what works best for your situation.
AWS security groups
Docker Machine will attempt to use a
default security group
with rules for port 2376
and SSH 22
, which is required for communication with the Docker
daemon. Instead of relying on Docker, you can create a security group with the
rules you need and provide that in the GitLab Runner options as we will
see below. This way, you can customize it to your
liking ahead of time based on your networking environment.
You have to make sure that ports 2376
and 22
are accessible by the Runner Manager instance.
AWS credentials
You’ll need an AWS Access Key
tied to a user with permission to scale (EC2) and update the cache (via S3).
Create a new user with policies
for EC2 (AmazonEC2FullAccess) and S3. For more information about the
minimal permissions required for S3, see runners.cache.s3
. To be more secure,
you can disable console login for that user. Keep the tab open or copy paste the
security credentials in an editor as we’ll use them later during the
GitLab Runner configuration.
You can also create an EC2 instance profile with the required AmazonEC2FullAccess
and AmazonS3FullAccess
policies.
To provision new EC2 instances for the jobs’ execution, attach this instance profile to the runner manager EC2 instance. If the runner machine is using an instance profile, include the iam:PassRole
action in the instance profile of the runner manager.
Example:
{
"Statement": [
{
"Action": "iam:PassRole",
"Effect": "Allow",
"Resource": "arn:aws:iam:::role/instance-profile-of-runner-machine"
}
],
"Version": "2012-10-17"
}
Prepare the runner manager instance
The first step is to install GitLab Runner in an EC2 instance that will serve as the runner manager that spawns new machines. Choose a distribution that both Docker and GitLab Runner support, like Ubuntu, Debian, CentOS, or RHEL.
This doesn’t have to be a powerful machine because a runner manager instance doesn’t run jobs itself. For your initial configuration, you can start with a smaller instance. This machine is a dedicated host because we need it always up and running. Therefore, it is the only host with an ongoing baseline cost.
Install the prerequisites:
- Log in to your server
- Install GitLab Runner from the official GitLab repository
- Install Docker
- Install Docker Machine from the GitLab fork (Docker has deprecated Docker Machine)
Now that the Runner is installed, it’s time to register it.
Registering the GitLab Runner
Before configuring the GitLab Runner, you need to first register it, so that it connects with your GitLab instance:
- Obtain a runner token
- Register the runner
- When asked the executor type, enter
docker+machine
You can now move on to the most important part, configuring the GitLab Runner.
Configuring the runner
Now that the runner is registered, you need to edit its configuration file and add the required options for the AWS machine driver.
Let’s first break it down to pieces.
The global section
In the global section, you can define the limit of the jobs that can be run
concurrently across all runners (concurrent
). This heavily depends on your
needs, like how many users GitLab Runner will accommodate, how much time your
builds take, etc. You can start with something low like 10
, and increase or
decrease its value going forward.
The check_interval
option defines how often the runner should check GitLab
for new jobs, in seconds.
Example:
concurrent = 10
check_interval = 0
Other options are also available.
The runners
section
From the [[runners]]
section, the most important part is the executor
which
must be set to docker+machine
. Most of those settings are taken care of when
you register the runner for the first time.
limit
sets the maximum number of machines (running and idle) that this runner
will spawn. For more information, check the
relationship between limit
, concurrent
and IdleCount
.
Example:
[[runners]]
name = "gitlab-aws-autoscaler"
url = "<URL of your GitLab instance>"
token = "<Runner's token>"
executor = "docker+machine"
limit = 20
Other options
under [[runners]]
are also available.
The runners.docker
section
In the [runners.docker]
section you can define the default Docker image to
be used by the child runners if it’s not defined in .gitlab-ci.yml
.
By using privileged = true
, all runners will be able to run
Docker in Docker
which is useful if you plan to build your own Docker images via GitLab CI/CD.
Next, we use disable_cache = true
to disable the Docker executor’s inner
cache mechanism since we will use the distributed cache mode as described
in the following section.
Example:
[runners.docker]
image = "alpine"
privileged = true
disable_cache = true
Other options
under [runners.docker]
are also available.
The runners.cache
section
To speed up your jobs, GitLab Runner provides a cache mechanism where selected directories and/or files are saved and shared between subsequent jobs. While not required for this setup, it is recommended to use the distributed cache mechanism that GitLab Runner provides. Since new instances will be created on demand, it is essential to have a common place where the cache is stored.
In the following example, we use Amazon S3:
[runners.cache]
Type = "s3"
Shared = true
[runners.cache.s3]
ServerAddress = "s3.amazonaws.com"
AccessKey = "<your AWS Access Key ID>"
SecretKey = "<your AWS Secret Access Key>"
BucketName = "<the bucket where your cache should be kept>"
BucketLocation = "us-east-1"
Here’s some more information to further explore the cache mechanism:
- Reference for
runners.cache
- Reference for
runners.cache.s3
- Deploying and using a cache server for GitLab Runner
- How cache works
The runners.machine
section
This is the most important part of the configuration and it’s the one that tells GitLab Runner how and when to spawn new or remove old Docker Machine instances.
We will focus on the AWS machine options, for the rest of the settings read about the:
- Autoscaling algorithm and the parameters it’s based on - depends on the needs of your organization
- Autoscaling periods - useful when there are regular time periods in your organization when no work is done, for example weekends
Here’s an example of the runners.machine
section:
[runners.machine]
IdleCount = 1
IdleTime = 1800
MaxBuilds = 10
MachineDriver = "amazonec2"
MachineName = "gitlab-docker-machine-%s"
MachineOptions = [
"amazonec2-access-key=XXXX",
"amazonec2-secret-key=XXXX",
"amazonec2-region=us-central-1",
"amazonec2-vpc-id=vpc-xxxxx",
"amazonec2-subnet-id=subnet-xxxxx",
"amazonec2-zone=x",
"amazonec2-use-private-address=true",
"amazonec2-tags=runner-manager-name,gitlab-aws-autoscaler,gitlab,true,gitlab-runner-autoscale,true",
"amazonec2-security-group=xxxxx",
"amazonec2-instance-type=m4.2xlarge",
]
[[runners.machine.autoscaling]]
Periods = ["* * 9-17 * * mon-fri *"]
IdleCount = 50
IdleTime = 3600
Timezone = "UTC"
[[runners.machine.autoscaling]]
Periods = ["* * * * * sat,sun *"]
IdleCount = 5
IdleTime = 60
Timezone = "UTC"
The Docker Machine driver is set to amazonec2
and the machine name has a
standard prefix followed by %s
(required) that is replaced by the ID of the
child runner: gitlab-docker-machine-%s
.
Now, depending on your AWS infrastructure, there are many options you can set up
under MachineOptions
. Below you can see the most common ones.
Machine option | Description |
---|---|
amazonec2-access-key=XXXX
| The AWS access key of the user that has permissions to create EC2 instances, see AWS credentials. |
amazonec2-secret-key=XXXX
| The AWS secret key of the user that has permissions to create EC2 instances, see AWS credentials. |
amazonec2-region=eu-central-1
| The region to use when launching the instance. You can omit this entirely and the default us-east-1 will be used.
|
amazonec2-vpc-id=vpc-xxxxx
| Your VPC ID to launch the instance in. |
amazonec2-subnet-id=subnet-xxxx
| The AWS VPC subnet ID. |
amazonec2-zone=x
| If not specified, the availability zone is a , it needs to be set to the same availability zone as the specified subnet, for example when the zone is eu-west-1b it has to be amazonec2-zone=b
|
amazonec2-use-private-address=true
| Use the private IP address of Docker Machines, but still create a public IP address. Useful to keep the traffic internal and avoid extra costs. |
amazonec2-tags=runner-manager-name,gitlab-aws-autoscaler,gitlab,true,gitlab-runner-autoscale,true
| AWS extra tag key-value pairs, useful to identify the instances on the AWS console. The “Name” tag is set to the machine name by default. We set the “runner-manager-name” to match the runner name set in [[runners]] , so that we can filter all the EC2 instances created by a specific manager setup.
|
amazonec2-security-group=xxxx
| AWS VPC security group name, not the security group ID. See AWS security groups. |
amazonec2-instance-type=m4.2xlarge
| The instance type that the child runners will run on. |
amazonec2-ssh-user=xxxx
| The user that will have SSH access to the instance. |
amazonec2-iam-instance-profile=xxxx_runner_machine_inst_profile_name
| The IAM instance profile to use for the runner machine. |
amazonec2-ami=xxxx_runner_machine_ami_id
| The GitLab Runner AMI ID for a specific image. |
amazonec2-request-spot-instance=true
| Use spare EC2 capacity that is available for less than the on-demand price. |
amazonec2-spot-price=xxxx_runner_machine_spot_price=x.xx
| Spot instance bid price (in US dollars). Requires the --amazonec2-request-spot-instance flag set to true . If you omit the amazonec2-spot-price , Docker Machine sets the maximum price to a default value of $0.50 per hour.
|
amazonec2-security-group-readonly=true
| Set the security group to read-only. |
amazonec2-userdata=xxxx_runner_machine_userdata_path
| Specify the runner machine userdata path.
|
amazonec2-root-size=XX
| The root disk size of the instance (in GB). |
Notes:
- Under
MachineOptions
you can add anything that the AWS Docker Machine driver supports. You are highly encouraged to read Docker’s docs as your infrastructure setup may warrant different options to be applied. - The child instances will use by default Ubuntu 16.04 unless you choose a
different AMI ID by setting
amazonec2-ami
. Set only supported base operating systems for Docker Machine. - If you specify
amazonec2-private-address-only=true
as one of the machine options, your EC2 instance won’t get assigned a public IP. This is ok if your VPC is configured correctly with an Internet Gateway (IGW) and routing is fine, but it’s something to consider if you’ve got a more complex configuration. Read more in Docker docs about VPC connectivity.
Other options
under [runners.machine]
are also available.
Getting it all together
Here’s the full example of /etc/gitlab-runner/config.toml
:
concurrent = 10
check_interval = 0
[[runners]]
name = "gitlab-aws-autoscaler"
url = "<URL of your GitLab instance>"
token = "<runner's token>"
executor = "docker+machine"
limit = 20
[runners.docker]
image = "alpine"
privileged = true
disable_cache = true
[runners.cache]
Type = "s3"
Shared = true
[runners.cache.s3]
ServerAddress = "s3.amazonaws.com"
AccessKey = "<your AWS Access Key ID>"
SecretKey = "<your AWS Secret Access Key>"
BucketName = "<the bucket where your cache should be kept>"
BucketLocation = "us-east-1"
[runners.machine]
IdleCount = 1
IdleTime = 1800
MaxBuilds = 100
MachineDriver = "amazonec2"
MachineName = "gitlab-docker-machine-%s"
MachineOptions = [
"amazonec2-access-key=XXXX",
"amazonec2-secret-key=XXXX",
"amazonec2-region=us-central-1",
"amazonec2-vpc-id=vpc-xxxxx",
"amazonec2-subnet-id=subnet-xxxxx",
"amazonec2-use-private-address=true",
"amazonec2-tags=runner-manager-name,gitlab-aws-autoscaler,gitlab,true,gitlab-runner-autoscale,true",
"amazonec2-security-group=XXXX",
"amazonec2-instance-type=m4.2xlarge",
]
[[runners.machine.autoscaling]]
Periods = ["* * 9-17 * * mon-fri *"]
IdleCount = 50
IdleTime = 3600
Timezone = "UTC"
[[runners.machine.autoscaling]]
Periods = ["* * * * * sat,sun *"]
IdleCount = 5
IdleTime = 60
Timezone = "UTC"
Cutting down costs with Amazon EC2 Spot instances
As described by Amazon:
Amazon EC2 Spot instances allow you to bid on spare Amazon EC2 computing capacity. Since Spot instances are often available at a discount compared to On-Demand pricing, you can significantly reduce the cost of running your applications, grow your application’s compute capacity and throughput for the same budget, and enable new types of cloud computing applications.
In addition to the runners.machine
options
you picked above, in /etc/gitlab-runner/config.toml
under the MachineOptions
section, add the following:
MachineOptions = [
"amazonec2-request-spot-instance=true",
"amazonec2-spot-price=",
]
In this configuration with an empty amazonec2-spot-price
, AWS sets your
bidding price for a Spot instance to the default On-Demand price of that
instance class. If you omit the amazonec2-spot-price
completely, Docker
Machine will set the maximum price to a
default value of $0.50 per hour.
You may further customize your Spot instance request:
MachineOptions = [
"amazonec2-request-spot-instance=true",
"amazonec2-spot-price=0.03",
"amazonec2-block-duration-minutes=60"
]
With this configuration, Docker Machines are created using Spot instances with a
maximum Spot request price of $0.03 per hour and the duration of the Spot instance
is capped at 60 minutes. The 0.03
number mentioned above is just an example, so
be sure to check on the current pricing based on the region you picked.
To learn more about Amazon EC2 Spot instances, visit the following links:
- https://aws.amazon.com/ec2/spot/
- https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-requests.html
- https://aws.amazon.com/ec2/spot/getting-started/
Caveats of Spot instances
While Spot instances is a great way to use unused resources and minimize the costs of your infrastructure, you must be aware of the implications.
Running CI jobs on Spot instances may increase the failure rates because of the Spot instances pricing model. If the maximum Spot price you specify exceeds the current Spot price you will not get the capacity requested. Spot pricing is revised on an hourly basis. Any existing Spot instances that have a maximum price below the revised Spot instance price will be terminated within two minutes and all jobs on Spot hosts will fail.
As a consequence, the auto-scale Runner would fail to create new machines while it will continue to request new instances. This eventually will make 60 requests and then AWS won’t accept any more. Then once the Spot price is acceptable, you are locked out for a bit because the call amount limit is exceeded.
If you encounter that case, you can use the following command in the runner manager machine to see the Docker Machines state:
docker-machine ls -q --filter state=Error --format "{{.NAME}}"
The GitLab fork does not support AWS EC2 fleets and their use with spot instances. As an alternative, you can use the Continuous Kernel Integration Project’s downstream fork.
Conclusion
In this guide we learned how to install and configure a GitLab Runner in autoscale mode on AWS.
Using the autoscale feature of GitLab Runner can save you both time and money. Using the Spot instances that AWS provides can save you even more, but you must be aware of the implications. As long as your bid is high enough, there shouldn’t be an issue.
You can read the following use cases from which this tutorial was (heavily) influenced: