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CI pipeline management for DCI jobs


To install dci-pipeline on a RHEL 8 system, do the following as root:

# dnf install -y
# cat > /etc/yum.repos.d/ansible-runner.repo <<EOF
name=Ansible Runner for EL 8 - $basearch
# dnf install -y dci-pipeline

Folders and files location

Once dci-pipeline package is installed, the files and resources you can find in this repository will be placed in the following locations:

  • /etc/dci-pipeline only contains an empty pipeline.yml file.
  • /etc/sysconfig folder contains the content of sysconfig folder, which is dci-pipeline file.
  • /etc/bash_completion.d contains a dci-queue file related to this tool, which comes from dciqueue/dci-queue.bash_completion file.
  • /usr/share/dci-pipeline contains the following scripts from tools folder in this repo: alert, common, dci-pipeline-helper, extract-dependencies, get-config-entry, loop_until_failure, loop_until_success, send_status, send_comment, test-runner and yaml2json. If podman flavour is selected, then <script>-podman scripts are placed here too.
  • /usr/bin folder holds scripts such as dci-pipeline, dci-auto-launch, dci-pipeline-schedule, dci-pipeline-check, dci-queue, dci-agent-ctl, dci-rebuild-pipeline, dci-settings2pipeline and dci-diff-pipeline. If podman flavour is selected, then <script>-podman scripts are placed here too. Note that some of these scripts come from tools folder, and others are generated from folders like dciagent, dcipipeline or dciqueue, and their podman flavours are located in container folder.

Also, have in mind that:

  • dci-pipeline user (with sudo permissions) and group are created. There's also a group for dci-queue.
  • Files under systemd folder in this repo will be used to create the corresponding system service for dci-pipeline.

dci-pipeline command

The dci-pipeline command allows to execute multiple DCI jobs using different credentials. Jobs are grouped by stage. Each stage must complete successfully before the next stage is started. The jobs with their stages are described in YAML files that are passed to the dci-pipeline command line. For example:

$ dci-pipeline dcipipeline/pipeline.yml
$ dci-pipeline dcipipeline/pipeline-retry.yml dcipipeline/cnf-pipeline.yml

Here is a pipeline example:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/agent.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp?tags:build:ga&name:4.14.1
      - plugin=1.1.1

Pipeline jobs can be split in multiple files to ease re-usability. To do so the pipeline names must be identical in the 2 files and the files must be loaded in order.


  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/agent.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp?tags%3abuild%3aga&name:4.14.1

and the next file could be like that to have same result as the first pipeline example:

  - name: openshift-vanilla
      - plugin=1.1.1

Select components with wildcards

This feature allows to select a component from one of its fields using wildcards. For example, to select using a prefix:

  - name: openshift-vanilla
    - "repo?name:Microshift 4.14*"

Schedule components by priority tags and age

this feature allows to select more precisely the components based on their tags and their age. Here is an example:

  - name: openshift-vanilla
    - type: ocp
        - build:ga
        - build:rc
        - build:dev
        - build:nightly
      max_age: 1
  • type the component type as usual
  • max_age variable indicates the maximum age the component in term of days
  • priority_tags this is the list of tags ordered by priority

Changing settings from the command line

Any part in the pipeline files can be overridden on the command line using the syntax <job name>:<field>=<value>. For example if you want to change the playbook to use in the openshift-vanilla job from the previous example, use:

$ dci-pipeline openshift-vanilla:ansible_playbook=/tmp/myplaybook.yml ~/pipelines/ocp-vanilla-pipeline.yml

Changing the name of the pipeline

To change the name of the pipeline, you can use the special setting @pipeline:name=<pipeline name>. For example:

$ dci-pipeline @pipeline:name=mypipeline mypipeline.yml

Setting the id of the pipeline (advanced)

To set the id of the pipeline, you can use the special setting @pipeline:pipeline_id=<pipeline id>. For example:

$ dci-pipeline @pipeline:pipeline_id=$PIPELINE_ID mypipeline.yml

It can be useful if you want to reconnect an existing pipeline. You should also set the previous_job_id of your first job to be connected at the right job in the previous pipeline.

Dynamic inventory (advanced)

If you need to create the inventory dynamically, you can use the inventory_playbook to generate the inventory. For example:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    inventory_playbook: ~/config/inventory.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    ansible_inventory: ~/inventories/agent.yml
      my_var: my_value
    topic: OCP-4.14
      - ocp

The inventory_playbook is called before the ansible_playbook without inventory so it can only be used to create the inventory file from the localhost. The inventory_playbook is called with the extra_vars, the ansible_inventory variable and, the DCI job information. Its output is visible in the DCI job output.

Calling ansible-playbook sub commands (advanced)

If you need to call the ansible-playbook command in your own playbook, the tasks will be captured automatically by the DCI callback mechanism. If you need to get the extra_vars defined in the job definition or passed on the command line of dci-pipeline, you can use the DCI_PLAYBOOK_ARGS environment variable. Example:

    - name: "Start VM and generate inventory" |
        set -eux -o pipefail
        ansible-playbook $DCI_PLAYBOOK_ARGS {{ samples_dir }}/ocp_on_libvirt/libvirt_up.yml -i {{ inventory_dir }}


dci-pipeline runs the jobs in their own workspace in /var/lib/dci-pipeline/<job name>/<job id>. Various log files are saved in this directory to ease debugging the DCI jobs.

Sharing information between jobs

The only way to share information between jobs is to use the output/input mechanism. This mechanism allows a job to export a file and have another job make use of this exported file.

For example, a first job will export a kubeconfig file:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/agent.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp
      kubecfg: "kubeconfig"

dci-pipeline will export a job_info.outputs dictionary with a kubecfg key. Here is an example on how to use it in the dci-openshift-agent.yml playbook specified in the example pipeline:

- name: set outputs to be copied
      kubecfg: "~/clusterconfigs/auth/kubeconfig"

- name: Copy outputs if defined
  delegate_to: "{{ groups['provisioner'][0] }}"
    src: "{{ outputs[item.key] }}"
    dest: "{{ item.value }}"
    flat: true
  with_dict: "{{ job_info.outputs }}"
  when: job_info.outputs is defined and job_info.outputs != None

Then to get the file of the kubecfg field from a previous job copied into the job workspace and its path stored into the kubeconfig_path variable, you have to define an inputs field and a prev_stages field to specify the stages or names of the previous jobs to lookup for a corresponding outputs like this:

- name: example-cnf
  stage: cnf
  prev_stages: [ocp]
  ansible_playbook: /usr/share/dci-openshift-app-agent/dci-openshift-app-agent.yml
  dci_credentials: /etc/dci-openshift-app-agent/dci_credentials.yml
  topic: OCP-4.14
  components: []
    kubecfg: kubeconfig_path

The path of the kubecfg file will be copied by dci-pipeline in the kubeconfig_path variable that is passed to the playbook. Here is an example on how to use it:

- name: "Check if KUBECONFIG exists"
    path: "{{ kubeconfig_path }}"
  register: kubeconfig

- name: "Fail if kubeconfig NOT found"
    msg: "kubeconfig not found at {{ kubeconfig_path }}"
  when: kubeconfig.stat.exists == False

Tagging and retrying

dci-pipeline can tag components on successful jobs by specifying a success_tag in the job definition. Example:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/agent.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp
    success_tag: ocp-vanilla-4.14-ok

If you want the job to restart on failure to the last known good components, you can specify fallback_last_success to lookup components with these tags. This is useful to be sure to have a successful job for a group even when a new delivery is broken to continue testing the next layers in the pipeline. Example:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/agent.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp
      - cnf
    success_tag: ocp-vanilla-4.14-ok
      - ocp-vanilla-4.14-ok
      - ocp?build:ga

In this example, when a fallback happens, dci-pipeline looks up the ocp-vanilla-4.14-ok and build:ga tags for the ocp component and only the ocp-vanilla-4.14-ok tag for the cnf component.

Passing environment variables

If you want to pass environment variables to the agent. Example:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/agent.yml
      answer: 42
      ENVVAR_42: 42
      ENVVAR_43: 43
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp
    success_tag: ocp-vanilla-4.14-ok
    fallback_last_success: ocp-vanilla-4.14-ok

Instrument the pipeline with temporary directories

You can specify the meta value "/@tmpdir" that will be replaced by an actual path of a temporary directory. Example:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/agent.yml
      answer: 42
      ENVVAR_42: 42
      ENVVAR_43: 43
      MY_TMP_DIR: /@tmpdir
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp
    success_tag: ocp-vanilla-4.14-ok
    fallback_last_success: ocp-vanilla-4.14-ok

This will create a new temporary directory before running the job, at the end of the job the directory is removed.

Special environment variables

dci-pipeline is setting the following environment variables to enable the junit Ansible callback to work out of the box:

      JUNIT_OUTPUT_DIR: /@tmpdir

You can override them if you need.

Using Ansible variable files

You can specify extra Ansible variable files using the ansible_extravars_files key in your pipeline file. Example:

  - name: openshift-vanilla
    stage: ocp
    ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
    ansible_inventory: ~/inventories/cluster.yml
      - agents/openshift-vanilla/vars.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    topic: OCP-4.14
      - ocp

Feeding variables from the command line

You can feed variables from the command line to the dci-pipeline-schedule and dci-pipeline-check commands that we explain below.

Variables set this way take the highest precedence and override those set in the pipeline definition files.

Each variable is set with a string of the format:

<jobdef name>:<key>=<value>


  • jobdef name: is the name set in the pipeline stage definition.

  • key: is the top name of the stage variable you are overriding. For instance: topic or components.

  • value: is the value you want to set.

On the other hand, several formats are accepted for the value that will be handled differently:

  • <jobdef name>:<key>=<value> just a literal text string. For instance, when defining the topic.

  • <jobdef name>:<key>=<value1>,<value2>,... a command-separated list of values with no blank spaces between items is parsed as a string. To define a one element list, the value must be followed by a comma, even if there's not second element. For instance, when defining more than one variables file.

  • <jobdef name>:<key>=<subkey>:<value> a value comprising two strings separated by a colon is parsed as a dictionary with a subkey and a value. If the subkey already exists in the pipeline manifest, it is overridden. If it does not exists, the subkey is added under the key, so any other subkey already existing is kept. For instance, when defining the components.

  • <jobdef name>:<key>='{"json":"string"}' a value formatted as a json text string is parsed as such resulting in a composite object. In this case, surrounding the json string or the entire parameter with single quotes is mandatory or the shell interpreter may try to apply brace expansion, thus breaking the json formatting.

The following is a usage example:

$ dci-pipeline-schedule ocp-vanilla workload \
    ocp-vanilla:topic=OCP-4.14 \
    workload:components=operator1,operator2 \
    workload:ansible_extravars=dci_tags:example \

Previous Topic

In a multi-stage pipeline, you can inherit the topic from the previous stage by using use_previous_topic in the configuration of the job. Example:

  - name: workload
    stage: app
    ansible_playbook: /usr/share/dci-openshift-app-agent/dci-openshift-app-agent.yml
    ansible_inventory: ~/inventories/app.yml
    dci_credentials: ~/.config/dci-pipeline/dci_credentials.yml
    use_previous_topic: true


dci-pipeline-schedule is a wrapper to call dci-pipeline without specifying the paths for the pipeline files and the inventories. This allows to have a more concise syntax.

For example, to do the equivalent of:

$ dci-pipeline ~/pipelines/ocp-vanilla-pipeline.yml ~/pipelines/workload-pipeline.yml


$ dci-pipeline-schedule ocp-vanilla workload

For this to work, you need to configure PIPELINES_DIR in one these files: ~/.config/dci-pipeline/config or /etc/dci-pipeline/config. Example:


You can also define the default dci-queue queue with the DEFAULT_QUEUE variable. To schedule on a specific dci-queue pool, use -p like this:

$ dci-pipeline-schedule -p my-pool ocp-vanilla workload


To test a Github PR, with specific a pipeline you can use dci-pipeline-check utility like that:

$ dci-pipeline-check -p my-pool ocp-4.14-vanilla workload

If you use private GitHub repositories, you need to set the GITHUB_TOKEN variable in your configuration file to get the details using the GitHub API about the changes. Then you need either to set the GITHUB_SSH_ID variable to download the pull request using ssh or the GITHUB_LOGIN variable to use https. The ssh key needs to be without password for the automation to work.

For dci-pipeline-check to vote like a regular CI system on GitHub, set the variable GITHUB_VOTING_REPOS to a regexp matching the repositories you want to vote on in your configuratio file. Your GitHub token needs the rights to vote for this to work. If you don't want to have comments in Github, use the variable GITHUB_NO_COMMENT_REPOS as a regexp for the projects where you don't want to receive comments.

If you use multiple Github accounts for different Github projects, you can use the ~/.config/dci-pipeline/config.yaml or /etc/dci-pipeline/config.yaml files to define your specific configuration. Example:
    github_token: mytoken
    github_login: mylogin
    github_ssh_id: myid
    git_clone_options: myoption

It also works for a Gerrit review from :

$ dci-pipeline-check 19837 -p my-pool ocp-4.14-vanilla workload

To vote on Gerrit reviews, you need to set the GERRIT_SSH_ID to point to the ssh key name (key without password) and the GERRIT_SSH_LOGIN variables.

dci-pipeline-check uses the same configuration files as dci-pipeline-schedule.

If no DCI job has been created by dci-pipeline-check, it stores its working directory into /tmp/test-runner-$DATE-$ID to ease the debug later.

Testing on an existing OCP cluster

To test a change on the dci-openshift-app-agent without re-installing an OCP cluster, you need to pass an extra parameter to the dci-pipeline-check command pointng to the KUBECONFIG of the OCP cluster like this:

$ dci-pipeline-check 26269 /path/to/kubeconfig workload

This bypasses the queue mechanism and directly launches the application pipelines on the OCP cluster.

Dependencies between changes

Sometimes you also need multiple changes to be tested at the same time. To do so, add a Build-Depends or Depends-On field pointing to you GitHub PR or Gerrit review in your git commit or GitHub PR description like this:


See this example:


dci-auto-launch allows to automatically schedule pipelines based on strings in the description of the pull request or gerrit review. It is meant to be used on the stream of events from Github or Gerrit.

It relies on the configuration file ~/.config/dci-pipeline/auto.conf in the following format:

cmd = dci-pipeline-check @URL -p pool

cmd = dci-queue schedule wrkld -- env QUEUE_TOKEN=@RESOURCE dci-pipeline-check @URL -p cluster /path/to/kubeconfig

And then you can use a string like that in the description of the change:

TestLab: ocp-vanilla
TestWorkload: workload

That will schedule the pipelines ocp-vanilla and workload like this:

$ dci-pipeline-check <change URL> -p pool ocp-vanilla
$ dci-queue schedule wrkld -- env QUEUE_TOKEN=@RESOURCE dci-pipeline-check <change URL> -p cluster /path/to/kubeconfig workload

no-check and force-check

You can also specify a Test-Hint: field in the description of your change. This will direct dci-pipeline-check to test in a specific way:

  • Test-Hint: no-check do not run a check (useful in CI mode).
  • Test-Hint: force-check run a check even if there is no code change (useful in CI mode).

dci-pipeline-schedule and dci-pipeline-check are the links between dci-queue and dci-pipeline. These utilities are substituting the strings @QUEUE and @RESOURCE coming from dci-queue in the inventory path, the configuration, and the ansible_extravars of the dci-pipeline job definitions. Example:

  - name: workload
    stage: app
    ansible_playbook: /usr/share/dci-openshift-app-agent/dci-openshift-app-agent.yml
    ansible_inventory: ~/inventories/@QUEUE/@RESOURCE.yml
    configuration: "@QUEUE"
      custom_config: /path/to/config/for/@QUEUE/@RESOURCE-config.yml

dci-pipeline-schedule and dci-pipeline-check are also managing the topic of jobs if there are only dci-openshift-app-agent job definitions without any dci-openshift-agent job definitions in the pipeline. To do so they rely on the running cluster defined by the KUBECONFIG environment variable or the ansible variable kubeconfig_path defined either in the job definition or in the inventory (all.vars.kubeconfig_path). Then they deduce the topic of the job from the OpenShift version of the cluster.


dci-agent-ctl is thin layer on top of dci-pipeline to consume regular agent settings transparently.

$ dci-agent-ctl /etc/dci-openshift-agent/settings.yml /etc/dci-openshift-app-agent/settings.yml

will translate the settings in /etc/dci-openshift-agent/settings.yml and /etc/dci-openshift-app-agent/settings.yml into pipelines and call dci-pipeline on them.

to be compatible with dci-agent-ctl, setting.yml files must have the following fields:

dci_name: "<job-name-no-space>"
dci_agent: openshift

dci_agent is the name of the agent: rhel, openstack, openshift or openshift-app.

By default, the DCI credentials will be taken from the same location as the settings.yml file in a YAML file called dci_credentials.yml. The format must be following:

  DCI_CLIENT_ID: <remote ci id>
  DCI_API_SECRET: <remote ci secret>

The path to this file can be overridden in the settings.yml files like:

dci_name: "<job-name-no-space>"
dci_agent: openshift
dci_credentials: "/etc/dci/dci_credentials.yml"


To use the parsing capabilities of dci-agent-ctl and just output the pipeline file without executing dci-pipeline, use dci-settings2pipeline like this:

$ dci-settings2pipeline /etc/dci-openshift-agent/settings.yml /etc/dci-openshift-app-agent/settings.yml /tmp/pipelines.yml

dci-queue command

The dci-queue command allows to execute commands consuming resources from pools. These pools are specific to the user executing the commands.

Create a pool named 8nodes:

$ dci-queue add-pool 8nodes

Add resources cluster4 and cluster6 into the 8nodes pool:

$ dci-queue add-resource 8nodes cluster4
$ dci-queue add-resource 8nodes cluster6

Schedule a dci-pipeline command on the 8nodes pool at priority 1 (the highest the priority, the soonest it'll be executed):

$ dci-queue schedule -p 1 8nodes dci-pipeline openshift-vanilla:ansible_inventory=/etc/inventories/@RESOURCE pipeline.yml

The @RESOURCE is mandatory in the command line to be executed and it is replaced by the resource name at execution time.

Schedule a dci-pipeline command on the 8nodes pool waiting for the command to complete to have its exit code and having all the log on the console:

$ dci-queue -c -l DEBUG schedule -b 8nodes dci-pipeline openshift-vanilla:ansible_inventory=/etc/inventories/@RESOURCE pipeline.yml

List pools in the host

$ dci-queue list
The following pools were found:
Run the command below for the list of commands scheduled on your target pool:
  dci-queue list <pool>

List dci-queue:

$ dci-queue list 8nodes
Commands on the 8nodes pool:
1(p1): dci-pipeline openshift-vanilla:ansible_inventory=/etc/inventories/@RESOURCE pipeline.yml (wd: /home/dci-pipeline)

Run commands from a pool (using all the available resources):

$ dci-queue run 8nodes

The following environment variables are set when running a job:

  • DCI_QUEUE: name of the pool.
  • DCI_QUEUE_ID: id of the job.
  • DCI_QUEUE_JOBID: uniq id with <pool name>.<id of the job>

You can unschedule the command 1 from the pool 8nodes:

$ dci-queue unschedule 8nodes 1

Remove cluster4 from available resources in the 8nodes pool:

$ dci-queue remove-resource 8nodes cluster4 'reserved to debug blabla (fred)'

You can also force the removal of a resource with dci-queue remove-resource -f, so that the resource will no longer be available in the pool (you will need to reinclude it with dci-queue add-resource command). Example with cluster6:

$ dci-queue remove-resource -f 8nodes cluster6 'whatever reason'

Remove the 8nodes pool:

$ dci-queue remove-pool 8nodes

Interactions with dci-pipeline-check and dci-pipeline-schedule

When dci-pipeline-check and dci-pipeline-schedule are used in conjunction with dci-queue, they automatically schedule the commands to run through dci-queue. They also perform the substitution of the @QUEUE and @RESOURCE strings in the ansible_inventory, the configuration and the ansible_extravars settings of the jobs allowing to have flexible job definitions regarding inventories and configurations without having to change the command lines.

How to rebuild a pipeline

In case of a pipeline failure, one might need to rebuild the original one. The command dci-rebuild-pipeline can be used for this purpose. To do so, you need to get any job id that was part of the pipeline you want to rebuild.

Once this is get. You need to run, for example, the following command:

$ dci-rebuild-pipeline --job_id 2441f3a5-aa97-45e9-8122-36dfc6f17d84

At the end of the command you will get a file rebuilt-pipeline.yml in the current directory.

The rebuilt pipeline will pin the components version to the original one.

For instance instead of having this component list from the original pipeline:

  - ocp
  - ose-tests
  - cnf-tests

You will got:

  - ocp=ocp-4.14.0-0.nightly-20230701
  - ose-tests=ose-tests-20200628
  - cnf-tests=cnf-tests-20200628

This rebuilt pipeline can be used as a regular one with the dci-pipeline command.

If you do not specify --job_id, dci-rebuild-pipeline is using the last job that has run.

How to see components diff between two pipelines

In case of a pipeline failulre, one might check if some components has changed from the previous run. The command dci-diff-pipeline can be used for this purpose. To do so, you need to get two jobs that are part of each pipeline (it can be any of the pipeline's job).

Once you got the two job ids, you need to use a user that has access to every components of the pipelines, including teams components.

You can see the component differentiation with the following command:

$ dci-diff-pipeline --job_id_1 610953f7-ad4a-442c-a934-cd5506127ec9 --job_id_2 f7677627-5780-46f8-b35f-c4bd1f781d90
|              pipeline 1              |              pipeline 2              |       stage       |  component type  |          component 1           |          component 2           |
| 94a4b04f-4aa5-413b-a86f-eb651b563e0b | ef493b57-a02e-4c74-9f60-e951b181f1d4 | openshift-vanilla |       ocp        |  ocp-4.14.0-0.nightly-20230703  |  ocp-4.14.0-0.nightly-20230701  |
| 610953f7-ad4a-442c-a934-cd5506127ec9 | f7677627-5780-46f8-b35f-c4bd1f781d90 |       rh-cnf      |      rh-cnf      |  rh-cnf-0.1.nightly-20200708   |  rh-cnf-0.1.nightly-20200703   |

If you do not specify --job_id_1, dci-diff-pipeline looks up the last job.

If you do not specify --job_id_2, dci-diff-pipeline looks up the first job from the last pipeline with the same name as the one from --job_id_1.


Submit changes to


To run the tests, you need to have tox installed on your system.

There are 3 kinds of tests:

  • lint: static code checks using flake8 and black.
  • unit: runs unit tests.
  • functional: runs functional tests against a local dci-dev-env instance prepared with dev-setup/dci-telcoprovisioning.


If you want to setup a git pre-commit hook, which verify a few checks using before accepting a commit, do the following:

$ tox -epre-commit