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# Auto DevOps
DANGER: Auto DevOps is currently in **Beta** and _not recommended for production use_ .
> [Introduced][ce-37115] in GitLab 10.0.
Auto DevOps automatically detects, builds, tests, deploys, and monitors your
applications.
## Overview
With Auto DevOps, the software development process becomes easier to set up
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as every project can have a complete workflow from verification to monitoring
without needing to configure anything. Just push your code and GitLab takes
care of everything else. This makes it easier to start new projects and brings
consistency to how applications are set up throughout a company.
## Comparison to application platforms and PaaS
Auto DevOps provides functionality described by others as an application
platform or as a Platform as a Service (PaaS). It takes inspiration from the
innovative work done by [Heroku ](https://www.heroku.com/ ) and goes beyond it
in a couple of ways:
1. Auto DevOps works with any Kubernetes cluster, you're not limited to running
on GitLab's infrastructure (note that many features also work without Kubernetes).
1. There is no additional cost (no markup on the infrastructure costs), and you
can use a self-hosted Kubernetes cluster or Containers as a Service on any
public cloud (for example [Google Kubernetes Engine ](https://cloud.google.com/kubernetes-engine/ )).
1. Auto DevOps has more features including security testing, performance testing,
and code quality testing.
1. It offers an incremental graduation path. If you need advanced customizations
you can start modifying the templates without having to start over on a
completely different platform.
## Features
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Comprised of a set of stages, Auto DevOps brings these best practices to your
project in an easy and automatic way:
1. [Auto Build ](#auto-build )
1. [Auto Test ](#auto-test )
1. [Auto Code Quality ](#auto-code-quality )
1. [Auto SAST (Static Application Security Testing) ](#auto-sast )
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1. [Auto Dependency Scanning ](#auto-dependency-scanning )
1. [Auto Container Scanning ](#auto-container-scanning )
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1. [Auto Review Apps ](#auto-review-apps )
1. [Auto DAST (Dynamic Application Security Testing) ](#auto-dast )
1. [Auto Deploy ](#auto-deploy )
1. [Auto Browser Performance Testing ](#auto-browser-performance-testing )
1. [Auto Monitoring ](#auto-monitoring )
As Auto DevOps relies on many different components, it's good to have a basic
knowledge of the following:
- [Kubernetes ](https://kubernetes.io/docs/home/ )
- [Helm ](https://docs.helm.sh/ )
- [Docker ](https://docs.docker.com )
- [GitLab Runner ](https://docs.gitlab.com/runner/ )
- [Prometheus ](https://prometheus.io/docs/introduction/overview/ )
Auto DevOps provides great defaults for all the stages; you can, however,
[customize ](#customizing ) almost everything to your needs.
For an overview on the creation of Auto DevOps, read the blog post [From 2/3 of the Self-Hosted Git Market, to the Next-Generation CI System, to Auto DevOps ](https://about.gitlab.com/2017/06/29/whats-next-for-gitlab-ci/ ).
## Prerequisites
TIP: **Tip:**
For self-hosted installations, the easiest way to make use of Auto DevOps is to
install GitLab inside a Kubernetes cluster using the [GitLab Omnibus Helm Chart]
which automatically installs and configures everything you need!
To make full use of Auto DevOps, you will need:
1. **GitLab Runner** (needed for all stages) - Your Runner needs to be
configured to be able to run Docker. Generally this means using the
[Docker ](https://docs.gitlab.com/runner/executors/docker.html ) or [Kubernetes
executor](https://docs.gitlab.com/runner/executors/kubernetes.html), with
[privileged mode enabled ](https://docs.gitlab.com/runner/executors/docker.html#use-docker-in-docker-with-privileged-mode ).
The Runners do not need to be installed in the Kubernetes cluster, but the
Kubernetes executor is easy to use and is automatically autoscaling.
Docker-based Runners can be configured to autoscale as well, using [Docker
Machine](https://docs.gitlab.com/runner/install/autoscaling.html). Runners
should be registered as [shared Runners ](../../ci/runners/README.md#registering-a-shared-runner )
for the entire GitLab instance, or [specific Runners ](../../ci/runners/README.md#registering-a-specific-runner )
that are assigned to specific projects.
1. **Base domain** (needed for Auto Review Apps and Auto Deploy) - You will need
a domain configured with wildcard DNS which is gonna be used by all of your
Auto DevOps applications. [Read the specifics ](#auto-devops-base-domain ).
1. **Kubernetes** (needed for Auto Review Apps, Auto Deploy, and Auto Monitoring) -
To enable deployments, you will need Kubernetes 1.5+. You need a [Kubernetes cluster][kubernetes-clusters]
for the project, or a Kubernetes [default service template ](../../user/project/integrations/services_templates.md )
for the entire GitLab installation.
1. **A load balancer** - You can use NGINX ingress by deploying it to your
Kubernetes cluster using the
[`nginx-ingress` ](https://github.com/kubernetes/charts/tree/master/stable/nginx-ingress )
Helm chart.
1. **Wildcard TLS termination** - You can deploy the
[`kube-lego` ](https://github.com/kubernetes/charts/tree/master/stable/kube-lego )
Helm chart to your Kubernetes cluster to automatically issue certificates
for your domains using Let's Encrypt.
1. **Prometheus** (needed for Auto Monitoring) - To enable Auto Monitoring, you
will need Prometheus installed somewhere (inside or outside your cluster) and
configured to scrape your Kubernetes cluster. To get response metrics
(in addition to system metrics), you need to
[configure Prometheus to monitor NGINX ](../../user/project/integrations/prometheus_library/nginx_ingress.md#configuring-prometheus-to-monitor-for-nginx-ingress-metrics ).
The [Prometheus service ](../../user/project/integrations/prometheus.md )
integration needs to be enabled for the project, or enabled as a
[default service template ](../../user/project/integrations/services_templates.md )
for the entire GitLab installation.
NOTE: **Note:**
If you do not have Kubernetes or Prometheus installed, then Auto Review Apps,
Auto Deploy, and Auto Monitoring will be silently skipped.
### Auto DevOps base domain
The Auto DevOps base domain is required if you want to make use of [Auto
Review Apps](#auto-review-apps) and [Auto Deploy ](#auto-deploy ). It is defined
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either under the project's CI/CD settings while
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[enabling Auto DevOps ](#enabling-auto-devops ) or in instance-wide settings in
the CI/CD section.
It can also be set at the project or group level as a variable, `AUTO_DEVOPS_DOMAIN` .
A wildcard DNS A record matching the base domain is required, for example,
given a base domain of `example.com` , you'd need a DNS entry like:
```
*.example.com 3600 A 1.2.3.4
```
where `example.com` is the domain name under which the deployed apps will be served,
and `1.2.3.4` is the IP address of your load balancer; generally NGINX
([see prerequisites](#prerequisites)). How to set up the DNS record is beyond
the scope of this document; you should check with your DNS provider.
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Alternatively you can use free public services like [xip.io ](http://xip.io ) or
[nip.io ](http://nip.io ) which provide automatic wildcard DNS without any
configuration. Just set the Auto DevOps base domain to `1.2.3.4.xip.io` or
`1.2.3.4.nip.io` .
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Once set up, all requests will hit the load balancer, which in turn will route
them to the Kubernetes pods that run your application(s).
NOTE: **Note:**
If GitLab is installed using the [GitLab Omnibus Helm Chart], there are two
options: provide a static IP, or have one assigned. For more information see the
relevant docs on the [network prerequisites ](../../install/kubernetes/gitlab_omnibus.md#networking-prerequisites ).
## Quick start
If you are using GitLab.com, see our [quick start guide ](quick_start_guide.md )
for using Auto DevOps with GitLab.com and an external Kubernetes cluster on
Google Cloud.
## Enabling Auto DevOps
If you haven't done already, read the [prerequisites ](#prerequisites ) to make
full use of Auto DevOps. If this is your fist time, we recommend you follow the
[quick start guide ](#quick-start ).
To enable Auto DevOps to your project:
1. Check that your project doesn't have a `.gitlab-ci.yml` , and remove it otherwise
1. Go to your project's **Settings > CI/CD > General pipelines settings** and
find the Auto DevOps section
1. Select "Enable Auto DevOps"
1. Optionally, but recommended, add in the [base domain ](#auto-devops-base-domain )
that will be used by Kubernetes to deploy your application
1. Hit **Save changes** for the changes to take effect
Once saved, an Auto DevOps pipeline will be triggered on the default branch.
NOTE: **Note:**
For GitLab versions 10.0 - 10.2, when enabling Auto DevOps, a pipeline needs to be
manually triggered either by pushing a new commit to the repository or by visiting
`https://example.gitlab.com/<username>/<project>/pipelines/new` and creating
a new pipeline for your default branch, generally `master` .
NOTE: **Note:**
If you are a GitLab Administrator, you can enable Auto DevOps instance wide
in **Admin Area > Settings > Continuous Integration and Deployment** . Doing that,
all the projects that haven't explicitly set an option will have Auto DevOps
enabled by default.
## Stages of Auto DevOps
The following sections describe the stages of Auto DevOps. Read them carefully
to understand how each one works.
### Auto Build
Auto Build creates a build of the application in one of two ways:
- If there is a `Dockerfile` , it will use `docker build` to create a Docker image.
- Otherwise, it will use [Herokuish ](https://github.com/gliderlabs/herokuish )
and [Heroku buildpacks ](https://devcenter.heroku.com/articles/buildpacks )
to automatically detect and build the application into a Docker image.
Either way, the resulting Docker image is automatically pushed to the
[Container Registry][container-registry] and tagged with the commit SHA.
CAUTION: **Important:**
If you are also using Auto Review Apps and Auto Deploy and choose to provide
your own `Dockerfile` , make sure you expose your application to port
`5000` as this is the port assumed by the default Helm chart.
### Auto Test
Auto Test automatically runs the appropriate tests for your application using
[Herokuish ](https://github.com/gliderlabs/herokuish ) and [Heroku
buildpacks](https://devcenter.heroku.com/articles/buildpacks) by analyzing
your project to detect the language and framework. Several languages and
frameworks are detected automatically, but if your language is not detected,
you may succeed with a [custom buildpack ](#custom-buildpacks ). Check the
[currently supported languages ](#currently-supported-languages ).
NOTE: **Note:**
Auto Test uses tests you already have in your application. If there are no
tests, it's up to you to add them.
### Auto Code Quality
Auto Code Quality uses the open source
[`codeclimate` image ](https://hub.docker.com/r/codeclimate/codeclimate/ ) to run
static analysis and other code checks on the current code. The report is
created, and is uploaded as an artifact which you can later download and check
out.
In GitLab Starter, differences between the source and
target branches are also
[shown in the merge request widget ](https://docs.gitlab.com/ee/user/project/merge_requests/code_quality_diff.html ).
### Auto SAST
> Introduced in [GitLab Ultimate][ee] 10.3.
Static Application Security Testing (SAST) uses the
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[SAST Docker image ](https://gitlab.com/gitlab-org/security-products/sast ) to run static
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analysis on the current code and checks for potential security issues. Once the
report is created, it's uploaded as an artifact which you can later download and
check out.
In GitLab Ultimate, any security warnings are also
[shown in the merge request widget ](https://docs.gitlab.com/ee/user/project/merge_requests/sast.html ).
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### Auto Dependency Scanning
> Introduced in [GitLab Ultimate][ee] 10.7.
Dependency Scanning uses the
[Dependency Scanning Docker image ](https://gitlab.com/gitlab-org/security-products/dependency-scanning )
to run analysis on the project dependencies and checks for potential security issues. Once the
report is created, it's uploaded as an artifact which you can later download and
check out.
In GitLab Ultimate, any security warnings are also
[shown in the merge request widget ](https://docs.gitlab.com/ee/user/project/merge_requests/dependency_scanning.html ).
### Auto Container Scanning
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> Introduced in GitLab 10.4.
Vulnerability Static Analysis for containers uses
[Clair ](https://github.com/coreos/clair ) to run static analysis on a
Docker image and checks for potential security issues. Once the report is
created, it's uploaded as an artifact which you can later download and
check out.
In GitLab Ultimate, any security warnings are also
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[shown in the merge request widget ](https://docs.gitlab.com/ee/user/project/merge_requests/container_scanning.html ).
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### Auto Review Apps
NOTE: **Note:**
This is an optional step, since many projects do not have a Kubernetes cluster
available. If the [prerequisites ](#prerequisites ) are not met, the job will
silently be skipped.
CAUTION: **Caution:**
Your apps should *not* be manipulated outside of Helm (using Kubernetes directly.)
This can cause confusion with Helm not detecting the change, and subsequent
deploys with Auto DevOps can undo your changes. Also, if you change something
and want to undo it by deploying again, Helm may not detect that anything changed
in the first place, and thus not realize that it needs to re-apply the old config.
[Review Apps][review-app] are temporary application environments based on the
branch's code so developers, designers, QA, product managers, and other
reviewers can actually see and interact with code changes as part of the review
process. Auto Review Apps create a Review App for each branch.
The Review App will have a unique URL based on the project name, the branch
name, and a unique number, combined with the Auto DevOps base domain. For
example, `user-project-branch-1234.example.com` . A link to the Review App shows
up in the merge request widget for easy discovery. When the branch is deleted,
for example after the merge request is merged, the Review App will automatically
be deleted.
### Auto DAST
> Introduced in [GitLab Ultimate][ee] 10.4.
Dynamic Application Security Testing (DAST) uses the
popular open source tool [OWASP ZAProxy ](https://github.com/zaproxy/zaproxy )
to perform an analysis on the current code and checks for potential security
issues. Once the report is created, it's uploaded as an artifact which you can
later download and check out.
In GitLab Ultimate, any security warnings are also
[shown in the merge request widget ](https://docs.gitlab.com/ee/user/project/merge_requests/dast.html ).
### Auto Browser Performance Testing
> Introduced in [GitLab Premium][ee] 10.4.
Auto Browser Performance Testing utilizes the [Sitespeed.io container ](https://hub.docker.com/r/sitespeedio/sitespeed.io/ ) to measure the performance of a web page. A JSON report is created and uploaded as an artifact, which includes the overall performance score for each page. By default, the root page of Review and Production environments will be tested. If you would like to add additional URL's to test, simply add the paths to a file named `.gitlab-urls.txt` in the root directory, one per line. For example:
```
/
/features
/direction
```
In GitLab Premium, performance differences between the source and target branches are [shown in the merge request widget ](https://docs.gitlab.com/ee/user/project/merge_requests/browser_performance_testing.html ).
### Auto Deploy
NOTE: **Note:**
This is an optional step, since many projects do not have a Kubernetes cluster
available. If the [prerequisites ](#prerequisites ) are not met, the job will
silently be skipped.
CAUTION: **Caution:**
Your apps should *not* be manipulated outside of Helm (using Kubernetes directly.)
This can cause confusion with Helm not detecting the change, and subsequent
deploys with Auto DevOps can undo your changes. Also, if you change something
and want to undo it by deploying again, Helm may not detect that anything changed
in the first place, and thus not realize that it needs to re-apply the old config.
After a branch or merge request is merged into the project's default branch (usually
`master` ), Auto Deploy deploys the application to a `production` environment in
the Kubernetes cluster, with a namespace based on the project name and unique
project ID, for example `project-4321` .
Auto Deploy doesn't include deployments to staging or canary by default, but the
[Auto DevOps template] contains job definitions for these tasks if you want to
enable them.
You can make use of [environment variables ](#helm-chart-variables ) to automatically
scale your pod replicas.
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It's important to note that when a project is deployed to a Kubernetes cluster,
it relies on a Docker image that has been pushed to the
[GitLab Container Registry ](../../user/project/container_registry.md ). Kubernetes
fetches this image and uses it to run the application. If the project is public,
the image can be accessed by Kubernetes without any authentication, allowing us
to have deployments more usable. If the project is private/internal, the
Registry requires credentials to pull the image. Currently, this is addressed
by providing `CI_JOB_TOKEN` as the password that can be used, but this token will
no longer be valid as soon as the deployment job finishes. This means that
Kubernetes can run the application, but in case it should be restarted or
executed somewhere else, it cannot be accessed again.
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### Auto Monitoring
NOTE: **Note:**
Check the [prerequisites ](#prerequisites ) for Auto Monitoring to make this stage
work.
Once your application is deployed, Auto Monitoring makes it possible to monitor
your application's server and response metrics right out of the box. Auto
Monitoring uses [Prometheus ](../../user/project/integrations/prometheus.md ) to
get system metrics such as CPU and memory usage directly from
[Kubernetes ](../../user/project/integrations/prometheus_library/kubernetes.md ),
and response metrics such as HTTP error rates, latency, and throughput from the
[NGINX server ](../../user/project/integrations/prometheus_library/nginx_ingress.md ).
The metrics include:
- **Response Metrics:** latency, throughput, error rate
- **System Metrics:** CPU utilization, memory utilization
If GitLab has been deployed using the [GitLab Omnibus Helm Chart], no
configuration is required.
If you have installed GitLab using a different method, you need to:
1. [Deploy Prometheus ](../../user/project/integrations/prometheus.md#configuring-your-own-prometheus-server-within-kubernetes ) into your Kubernetes cluster
1. If you would like response metrics, ensure you are running at least version
0.9.0 of NGINX Ingress and
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[enable Prometheus metrics ](https://github.com/kubernetes/ingress-nginx/blob/master/docs/examples/customization/custom-vts-metrics-prometheus/nginx-vts-metrics-conf.yaml ).
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1. Finally, [annotate ](https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/ )
the NGINX Ingress deployment to be scraped by Prometheus using
`prometheus.io/scrape: "true"` and `prometheus.io/port: "10254"` .
To view the metrics, open the
[Monitoring dashboard for a deployed environment ](../../ci/environments.md#monitoring-environments ).
## Customizing
While Auto DevOps provides great defaults to get you started, you can customize
almost everything to fit your needs; from custom [buildpacks ](#custom-buildpacks ),
to [`Dockerfile`s ](#custom-dockerfile ), [Helm charts ](#custom-helm-chart ), or
even copying the complete [CI/CD configuration ](#customizing-gitlab-ci-yml )
into your project to enable staging and canary deployments, and more.
### Custom buildpacks
If the automatic buildpack detection fails for your project, or if you want to
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use a custom buildpack, you can override the buildpack(s) using a project variable
or a `.buildpacks` file in your project:
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- **Project variable** - Create a project variable `BUILDPACK_URL` with the URL
of the buildpack to use.
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- **`.buildpacks` file** - Add a file in your project's repo called `.buildpacks`
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and add the URL of the buildpack to use on a line in the file. If you want to
use multiple buildpacks, you can enter them in, one on each line.
CAUTION: **Caution:**
Using multiple buildpacks isn't yet supported by Auto DevOps.
### Custom `Dockerfile`
If your project has a `Dockerfile` in the root of the project repo, Auto DevOps
will build a Docker image based on the Dockerfile rather than using buildpacks.
This can be much faster and result in smaller images, especially if your
Dockerfile is based on [Alpine ](https://hub.docker.com/_/alpine/ ).
### Custom Helm Chart
Auto DevOps uses [Helm ](https://helm.sh/ ) to deploy your application to Kubernetes.
You can override the Helm chart used by bundling up a chart into your project
repo or by specifying a project variable:
- **Bundled chart** - If your project has a `./chart` directory with a `Chart.yaml`
file in it, Auto DevOps will detect the chart and use it instead of the [default
one](https://gitlab.com/charts/charts.gitlab.io/tree/master/charts/auto-deploy-app).
This can be a great way to control exactly how your application is deployed.
- **Project variable** - Create a [project variable ](../../ci/variables/README.md#secret-variables )
`AUTO_DEVOPS_CHART` with the URL of a custom chart to use.
### Customizing `.gitlab-ci.yml`
If you want to modify the CI/CD pipeline used by Auto DevOps, you can copy the
[Auto DevOps template] into your project's repo and edit as you see fit.
Assuming that your project is new or it doesn't have a `.gitlab-ci.yml` file
present:
1. From your project home page, either click on the "Set up CI/CD" button, or click
on the plus button and (`+`), then "New file"
1. Pick `.gitlab-ci.yml` as the template type
1. Select "Auto-DevOps" from the template dropdown
1. Edit the template or add any jobs needed
1. Give an appropriate commit message and hit "Commit changes"
TIP: **Tip:** The Auto DevOps template includes useful comments to help you
customize it. For example, if you want deployments to go to a staging environment
instead of directly to a production one, you can enable the `staging` job by
renaming `.staging` to `staging` . Then make sure to uncomment the `when` key of
the `production` job to turn it into a manual action instead of deploying
automatically.
### PostgreSQL database support
In order to support applications that require a database,
[PostgreSQL][postgresql] is provisioned by default. The credentials to access
the database are preconfigured, but can be customized by setting the associated
[variables ](#environment-variables ). These credentials can be used for defining a
`DATABASE_URL` of the format:
```yaml
postgres://user:password@postgres-host:postgres-port/postgres-database
```
### Environment variables
The following variables can be used for setting up the Auto DevOps domain,
providing a custom Helm chart, or scaling your application. PostgreSQL can be
also be customized, and you can easily use a [custom buildpack ](#custom-buildpacks ).
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| **Variable** | **Description** |
| ------------ | --------------- |
| `AUTO_DEVOPS_DOMAIN` | The [Auto DevOps domain ](#auto-devops-domain ); by default set automatically by the [Auto DevOps setting ](#enabling-auto-devops ). |
| `AUTO_DEVOPS_CHART` | The Helm Chart used to deploy your apps; defaults to the one [provided by GitLab ](https://gitlab.com/charts/charts.gitlab.io/tree/master/charts/auto-deploy-app ). |
| `REPLICAS` | The number of replicas to deploy; defaults to 1. |
| `PRODUCTION_REPLICAS` | The number of replicas to deploy in the production environment. This takes precedence over `REPLICAS` ; defaults to 1. |
| `CANARY_REPLICAS` | The number of canary replicas to deploy for [Canary Deployments ](https://docs.gitlab.com/ee/user/project/canary_deployments.html ); defaults to 1 |
| `CANARY_PRODUCTION_REPLICAS` | The number of canary replicas to deploy for [Canary Deployments ](https://docs.gitlab.com/ee/user/project/canary_deployments.html ) in the production environment. This takes precedence over `CANARY_REPLICAS` ; defaults to 1 |
| `POSTGRES_ENABLED` | Whether PostgreSQL is enabled; defaults to `"true"` . Set to `false` to disable the automatic deployment of PostgreSQL. |
| `POSTGRES_USER` | The PostgreSQL user; defaults to `user` . Set it to use a custom username. |
| `POSTGRES_PASSWORD` | The PostgreSQL password; defaults to `testing-password` . Set it to use a custom password. |
| `POSTGRES_DB` | The PostgreSQL database name; defaults to the value of [`$CI_ENVIRONMENT_SLUG` ](../../ci/variables/README.md#predefined-variables-environment-variables ). Set it to use a custom database name. |
| `BUILDPACK_URL` | The buildpack's full URL. It can point to either Git repositories or a tarball URL. For Git repositories, it is possible to point to a specific `ref` , for example `https://github.com/heroku/heroku-buildpack-ruby.git#v142` |
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| `STAGING_ENABLED` | From GitLab 10.8, this variable can be used to define a [deploy policy for staging and production environments ](#deploy-policy-for-staging-and-production-environments ). |
| `INCREMENTAL_ROLLOUT_ENABLED` | From GitLab 10.8, this variable can be used to enable an [incremental rollout ](#incremental-rollout-to-production ) of your application for the production environment. |
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TIP: **Tip:**
Set up the replica variables using a
[project variable ](../../ci/variables/README.md#secret-variables )
and scale your application by just redeploying it!
CAUTION: **Caution:**
You should *not* scale your application using Kubernetes directly. This can
cause confusion with Helm not detecting the change, and subsequent deploys with
Auto DevOps can undo your changes.
#### Advanced replica variables setup
Apart from the two replica-related variables for production mentioned above,
you can also use others for different environments.
There's a very specific mapping between Kubernetes' label named `track` ,
GitLab CI/CD environment names, and the replicas environment variable.
The general rule is: `TRACK_ENV_REPLICAS` . Where:
- `TRACK` : The capitalized value of the `track`
[Kubernetes label ](https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/ )
in the Helm Chart app definition. If not set, it will not be taken into account
to the variable name.
- `ENV` : The capitalized environment name of the deploy job that is set in
`.gitlab-ci.yml` .
That way, you can define your own `TRACK_ENV_REPLICAS` variables with which
you will be able to scale the pod's replicas easily.
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In the example below, the environment's name is `qa` and it deploys the track
`foo` which would result in looking for the `FOO_QA_REPLICAS` environment
variable:
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```yaml
QA testing:
stage: deploy
environment:
name: qa
script:
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- deploy foo
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```
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The track `foo` being referenced would also need to be defined in the
application's Helm chart, like:
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```yaml
replicaCount: 1
image:
repository: gitlab.example.com/group/project
tag: stable
pullPolicy: Always
secrets:
- name: gitlab-registry
application:
track: foo
tier: web
service:
enabled: true
name: web
type: ClusterIP
url: http://my.host.com/
externalPort: 5000
internalPort: 5000
```
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#### Deploy policy for staging and production environments
> [Introduced](https://gitlab.com/gitlab-org/gitlab-ci-yml/merge_requests/160)
in GitLab 10.8.
The normal behavior of Auto DevOps is to use Continuous Deployment, pushing
automatically to the `production` environment every time a new pipeline is run
on the default branch. However, there are cases where you might want to use a
staging environment and deploy to production manually. For this scenario, the
`STAGING_ENABLED` environment variable was introduced.
If `STAGING_ENABLED` is defined in your project (e.g., set `STAGING_ENABLED` to
`1` as a secret variable), then the application will be automatically deployed
to a `staging` environment, and a `production_manual` job will be created for
you when you're ready to manually deploy to production.
#### Incremental rollout to production **[PREMIUM]**
> [Introduced](https://gitlab.com/gitlab-org/gitlab-ee/issues/5415) in GitLab 10.8.
When you have a new version of your app to deploy in production, you may want
to use an incremental rollout to replace just a few pods with the latest code.
This will allow you to first check how the app is behaving, and later manually
increasing the rollout up to 100%.
If `INCREMENTAL_ROLLOUT_ENABLED` is defined in your project (e.g., set
`INCREMENTAL_ROLLOUT_ENABLED` to `1` as a secret variable), then instead of the
standard `production` job, 4 different
[manual jobs ](../../ci/pipelines.md#manual-actions-from-the-pipeline-graph )
will be created:
1. `rollout 10%`
1. `rollout 25%`
1. `rollout 50%`
1. `rollout 100%`
The percentage is based on the `REPLICAS` variable and defines the number of
pods you want to have for your deployment. If you say `10` , and then you run
the `10%` rollout job, there will be `1` new pod + `9` old ones.
To start a job, click on the play icon next to the job's name. You are not
required to go from `10%` to `100%` , you can jump to whatever job you want.
You can also scale down by running a lower percentage job, just before hitting
`100%` . Once you get to `100%` , you cannot scale down, and you'd have to roll
back by redeploying the old version using the
[rollback button ](../../ci/environments.md#rolling-back-changes ) in the
environment page.
Below, you can see how the pipeline will look if the rollout or staging
variables are defined.
- **Without `INCREMENTAL_ROLLOUT_ENABLED` and without `STAGING_ENABLED` **
- **Without `INCREMENTAL_ROLLOUT_ENABLED` and with `STAGING_ENABLED` **
- **With `INCREMENTAL_ROLLOUT_ENABLED` and without `STAGING_ENABLED` **
- **With `INCREMENTAL_ROLLOUT_ENABLED` and with `STAGING_ENABLED` **
2018-03-17 18:26:18 +05:30
## Currently supported languages
NOTE: **Note:**
Not all buildpacks support Auto Test yet, as it's a relatively new
enhancement. All of Heroku's [officially supported
languages](https://devcenter.heroku.com/articles/heroku-ci#currently-supported-languages)
support it, and some third-party buildpacks as well e.g., Go, Node, Java, PHP,
Python, Ruby, Gradle, Scala, and Elixir all support Auto Test, but notably the
multi-buildpack does not.
As of GitLab 10.0, the supported buildpacks are:
```
- heroku-buildpack-multi v1.0.0
- heroku-buildpack-ruby v168
- heroku-buildpack-nodejs v99
- heroku-buildpack-clojure v77
- heroku-buildpack-python v99
- heroku-buildpack-java v53
- heroku-buildpack-gradle v23
- heroku-buildpack-scala v78
- heroku-buildpack-play v26
- heroku-buildpack-php v122
- heroku-buildpack-go v72
- heroku-buildpack-erlang fa17af9
- buildpack-nginx v8
```
## Limitations
The following restrictions apply.
### Private project support
CAUTION: **Caution:** Private project support in Auto DevOps is experimental.
When a project has been marked as private, GitLab's [Container
Registry][container-registry] requires authentication when downloading
containers. Auto DevOps will automatically provide the required authentication
information to Kubernetes, allowing temporary access to the registry.
Authentication credentials will be valid while the pipeline is running, allowing
for a successful initial deployment.
After the pipeline completes, Kubernetes will no longer be able to access the
Container Registry. **Restarting a pod, scaling a service, or other actions which
require on-going access to the registry may fail**. On-going secure access is
planned for a subsequent release.
## Troubleshooting
- Auto Build and Auto Test may fail in detecting your language/framework. There
may be no buildpack for your application, or your application may be missing the
key files the buildpack is looking for. For example, for ruby apps, you must
have a `Gemfile` to be properly detected, even though it is possible to write a
Ruby app without a `Gemfile` . Try specifying a [custom
buildpack](#custom-buildpacks).
- Auto Test may fail because of a mismatch between testing frameworks. In this
case, you may need to customize your `.gitlab-ci.yml` with your test commands.
### Disable the banner instance wide
If an administrator would like to disable the banners on an instance level, this
feature can be disabled either through the console:
```sh
sudo gitlab-rails console
```
Then run:
```ruby
Feature.get(:auto_devops_banner_disabled).enable
```
Or through the HTTP API with an admin access token:
```sh
curl --data "value=true" --header "PRIVATE-TOKEN: personal_access_token" https://gitlab.example.com/api/v4/features/auto_devops_banner_disabled
```
[ce-37115]: https://gitlab.com/gitlab-org/gitlab-ce/issues/37115
[kubernetes-clusters]: ../../user/project/clusters/index.md
[docker-in-docker]: ../../docker/using_docker_build.md#use-docker-in-docker-executor
[review-app]: ../../ci/review_apps/index.md
[container-registry]: ../../user/project/container_registry.md
[postgresql]: https://www.postgresql.org/
[Auto DevOps template]: https://gitlab.com/gitlab-org/gitlab-ci-yml/blob/master/Auto-DevOps.gitlab-ci.yml
[GitLab Omnibus Helm Chart]: ../../install/kubernetes/gitlab_omnibus.md
[ee]: https://about.gitlab.com/products/
