Pipelines

Spack provides commands that support generating and running automated build pipelines designed for Gitlab CI. At the highest level it works like this: provide a spack environment describing the set of packages you care about, and include within that environment file a description of how those packages should be mapped to Gitlab runners. Spack can then generate a .gitlab-ci.yml file containing job descriptions for all your packages that can be run by a properly configured Gitlab CI instance. When run, the generated pipeline will build and deploy binaries, and it can optionally report to a CDash instance regarding the health of the builds as they evolve over time.

Getting started with pipelines

It is fairly straightforward to get started with automated build pipelines. At a minimum, you’ll need to set up a Gitlab instance (more about Gitlab CI here) and configure at least one runner. Then the basic steps for setting up a build pipeline are as follows:

  1. Create a repository on your gitlab instance

  2. Add a spack.yaml at the root containing your pipeline environment (see below for details)

  3. Add a .gitlab-ci.yml at the root containing two jobs (one to generate the pipeline dynamically, and one to run the generated jobs), similar to this one:

    stages: [generate, build]
    
    generate-pipeline:
      stage: generate
      tags:
        - <custom-tag>
      script:
        - spack env activate .
        - spack ci generate
          --output-file "${CI_PROJECT_DIR}/jobs_scratch_dir/pipeline.yml"
      artifacts:
        paths:
          - "${CI_PROJECT_DIR}/jobs_scratch_dir/pipeline.yml"
    
    build-jobs:
      stage: build
      trigger:
        include:
          - artifact: "jobs_scratch_dir/pipeline.yml"
            job: generate-pipeline
        strategy: depend
    
  4. Add any secrets required by the CI process to environment variables using the CI web ui

  5. Push a commit containing the spack.yaml and .gitlab-ci.yml mentioned above to the gitlab repository

The <custom-tag>, above, is used to pick one of your configured runners to run the pipeline generation phase (this is implemented in the spack ci generate command, which assumes the runner has an appropriate version of spack installed and configured for use). Of course, there are many ways to customize the process. You can configure CDash reporting on the progress of your builds, set up S3 buckets to mirror binaries built by the pipeline, clone a custom spack repository/ref for use by the pipeline, and more.

While it is possible to set up pipelines on gitlab.com, the builds there are limited to 60 minutes and generic hardware. It is also possible to hook up Gitlab to Google Kubernetes Engine (GKE) or Amazon Elastic Kubernetes Service (EKS), though those topics are outside the scope of this document.

Spack’s pipelines are now making use of the trigger syntax to run dynamically generated child pipelines. Note that the use of dynamic child pipelines requires running Gitlab version >= 12.9.

Spack commands supporting pipelines

Spack provides a command ci with two sub-commands: spack ci generate generates a pipeline (a .gitlab-ci.yml file) from a spack environment, and spack ci rebuild checks a spec against a remote mirror and possibly rebuilds it from source and updates the binary mirror with the latest built package. Both spack ci ... commands must be run from within the same environment, as each one makes use of the environment for different purposes. Additionally, some options to the commands (or conditions present in the spack environment file) may require particular environment variables to be set in order to function properly. Examples of these are typically secrets needed for pipeline operation that should not be visible in a spack environment file. These environment variables are described in more detail Environment variables affecting pipeline operation.

spack ci

Super-command for functionality related to generating pipelines and executing pipeline jobs.

spack ci generate

Concretizes the specs in the active environment, stages them (as described in Summary of .gitlab-ci.yml generation algorithm), and writes the resulting .gitlab-ci.yml to disk.

spack ci rebuild

This sub-command is responsible for ensuring a single spec from the release environment is up to date on the remote mirror configured in the environment, and as such, corresponds to a single job in the .gitlab-ci.yml file.

A pipeline-enabled spack environment

Here’s an example of a spack environment file that has been enhanced with sections describing a build pipeline:

spack:
  definitions:
  - pkgs:
    - readline@7.0
  - compilers:
    - '%gcc@5.5.0'
  - oses:
    - os=ubuntu18.04
    - os=centos7
  specs:
  - matrix:
    - [$pkgs]
    - [$compilers]
    - [$oses]
  mirrors:
    cloud_gitlab: https://mirror.spack.io
  gitlab-ci:
    mappings:
      - match:
          - os=ubuntu18.04
        runner-attributes:
          tags:
            - spack-kube
          image: spack/ubuntu-bionic
      - match:
          - os=centos7
        runner-attributes:
          tags:
            - spack-kube
          image: spack/centos7
  cdash:
    build-group: Release Testing
    url: https://cdash.spack.io
    project: Spack
    site: Spack AWS Gitlab Instance

Hopefully, the definitions, specs, mirrors, etc. sections are already familiar, as they are part of spack Environments. So let’s take a more in-depth look some of the pipeline-related sections in that environment file that might not be as familiar.

The gitlab-ci section is used to configure how the pipeline workload should be generated, mainly how the jobs for building specs should be assigned to the configured runners on your instance. Each entry within the list of mappings corresponds to a known gitlab runner, where the match section is used in assigning a release spec to one of the runners, and the runner-attributes section is used to configure the spec/job for that particular runner.

There are other pipeline options you can configure within the gitlab-ci section as well. The bootstrap section allows you to specify lists of specs from your definitions that should be staged ahead of the environment’s specs (this section is described in more detail below). The enable-artifacts-buildcache key takes a boolean and determines whether the pipeline uses artifacts to store and pass along the buildcaches from one stage to the next (the default if you don’t provide this option is False). The enable-debug-messages key takes a boolean and allows you to choose whether the pipeline build jobs are run as spack -d ci rebuild or just spack ci rebuild (the default is not to enable debug messages). The final-stage-rebuild-index section controls whether an extra job is added to the end of your pipeline (in a stage by itself) which will regenerate the mirror’s buildcache index. Under normal operation, each pipeline job that rebuilds a package will re-generate the mirror’s buildcache index after the buildcache entry for that job has been created and pushed to the mirror. Since jobs in the same stage can run in parallel, there is the possibility that at the end of some stage, the index may not reflect all the binaries in the buildcache. Adding the final-stage-rebuild-index section ensures that at the end of the pipeline, the index will be in sync with the binaries on the mirror. If the mirror lives in an S3 bucket, this job will need to run on a machine with the Python boto3 module installed, and consequently the final-stage-rebuild-index needs to specify a list of tags to pick a runner satisfying that condition. It can also take an image key so Docker executor type runners can pick the right image for the index regeneration job.

The optional cdash section provides information that will be used by the spack ci generate command (invoked by spack ci start) for reporting to CDash. All the jobs generated from this environment will belong to a “build group” within CDash that can be tracked over time. As the release progresses, this build group may have jobs added or removed. The url, project, and site are used to specify the CDash instance to which build results should be reported.

Assignment of specs to runners

The mappings section corresponds to a list of runners, and during assignment of specs to runners, the list is traversed in order looking for matches, the first runner that matches a release spec is assigned to build that spec. The match section within each runner mapping section is a list of specs, and if any of those specs match the release spec (the spec.satisfies() method is used), then that runner is considered a match.

Configuration of specs/jobs for a runner

Once a runner has been chosen to build a release spec, the runner-attributes section provides information determining details of the job in the context of the runner. The runner-attributes section must have a tags key, which is a list containing at least one tag used to select the runner from among the runners known to the gitlab instance. For Docker executor type runners, the image key is used to specify the Docker image used to build the release spec (and could also appear as a dictionary with a name specifying the image name, as well as an entrypoint to override whatever the default for that image is). For other types of runners the variables key will be useful to pass any information on to the runner that it needs to do its work (e.g. scheduler parameters, etc.).

Summary of .gitlab-ci.yml generation algorithm

All specs yielded by the matrix (or all the specs in the environment) have their dependencies computed, and the entire resulting set of specs are staged together before being run through the gitlab-ci/mappings entries, where each staged spec is assigned a runner. “Staging” is the name we have given to the process of figuring out in what order the specs should be built, taking into consideration Gitlab CI rules about jobs/stages. In the staging process the goal is to maximize the number of jobs in any stage of the pipeline, while ensuring that the jobs in any stage only depend on jobs in previous stages (since those jobs are guaranteed to have completed already). As a runner is determined for a job, the information in the runner-attributes is used to populate various parts of the job description that will be used by Gitlab CI. Once all the jobs have been assigned a runner, the .gitlab-ci.yml is written to disk.

The short example provided above would result in the readline, ncurses, and pkgconf packages getting staged and built on the runner chosen by the spack-k8s tag. In this example, we assume the runner is a Docker executor type runner, and thus certain jobs will be run in the centos7 container, and others in the ubuntu-18.04 container. The resulting .gitlab-ci.yml will contain 6 jobs in three stages. Once the jobs have been generated, the presence of a SPACK_CDASH_AUTH_TOKEN environment variable during the spack ci generate command would result in all of the jobs being put in a build group on CDash called “Release Testing” (that group will be created if it didn’t already exist).

Optional compiler bootstrapping

Spack pipelines also have support for bootstrapping compilers on systems that may not already have the desired compilers installed. The idea here is that you can specify a list of things to bootstrap in your definitions, and spack will guarantee those will be installed in a phase of the pipeline before your release specs, so that you can rely on those packages being available in the binary mirror when you need them later on in the pipeline. At the moment the only viable use-case for bootstrapping is to install compilers.

Here’s an example of what bootstrapping some compilers might look like:

spack:
  definitions:
  - compiler-pkgs:
    - 'llvm+clang@6.0.1 os=centos7'
    - 'gcc@6.5.0 os=centos7'
    - 'llvm+clang@6.0.1 os=ubuntu18.04'
    - 'gcc@6.5.0 os=ubuntu18.04'
  - pkgs:
    - readline@7.0
  - compilers:
    - '%gcc@5.5.0'
    - '%gcc@6.5.0'
    - '%gcc@7.3.0'
    - '%clang@6.0.0'
    - '%clang@6.0.1'
  - oses:
    - os=ubuntu18.04
    - os=centos7
  specs:
  - matrix:
    - [$pkgs]
    - [$compilers]
    - [$oses]
    exclude:
      - '%gcc@7.3.0 os=centos7'
      - '%gcc@5.5.0 os=ubuntu18.04'
  gitlab-ci:
    bootstrap:
      - name: compiler-pkgs
        compiler-agnostic: true
    mappings:
      # mappings similar to the example higher up in this description
      ...

In the example above, we have added a list to the definitions called compiler-pkgs (you can add any number of these), which lists compiler packages we want to be staged ahead of the full matrix of release specs (which consists only of readline in our example). Then within the gitlab-ci section, we have added a bootstrap section, which can contain a list of items, each referring to a list in the definitions section. These items can either be a dictionary or a string. If you supply a dictionary, it must have a name key whose value must match one of the lists in definitions and it can have a compiler-agnostic key whose value is a boolean. If you supply a string, then it needs to match one of the lists provided in definitions. You can think of the bootstrap list as an ordered list of pipeline “phases” that will be staged before your actual release specs. While this introduces another layer of bottleneck in the pipeline (all jobs in all stages of one phase must complete before any jobs in the next phase can begin), it also means you are guaranteed your bootstrapped compilers will be available when you need them.

The compiler-agnostic key can be provided with each item in the bootstrap list. It tells the spack ci generate command that any jobs staged from that particular list should have the compiler removed from the spec, so that any compiler available on the runner where the job is run can be used to build the package.

When including a bootstrapping phase as in the example above, the result is that the bootstrapped compiler packages will be pushed to the binary mirror (and the local artifacts mirror) before the actual release specs are built. In this case, the jobs corresponding to subsequent release specs are configured to install_missing_compilers, so that if spack is asked to install a package with a compiler it doesn’t know about, it can be quickly installed from the binary mirror first.

Since bootstrapping compilers is optional, those items can be left out of the environment/stack file, and in that case no bootstrapping will be done (only the specs will be staged for building) and the runners will be expected to already have all needed compilers installed and configured for spack to use.

Using a custom spack in your pipeline

If your runners will not have a version of spack ready to invoke, or if for some other reason you want to use a custom version of spack to run your pipelines, this can be accomplished fairly simply. First, create CI environment variables containing the url and branch/tag you want to clone (calling them, for example, SPACK_REPO and SPACK_REF), use them to clone spack in your pre-ci before_script, and finally pass those same values along to the workload generation process via the spack-repo and spack-ref cli args. Here’s the generate-pipeline job from the top of this document, updated to clone a custom spack and make sure the generated rebuild jobs will clone it too:

generate-pipeline:
  tags:
    - <some-other-tag>
before_script:
  - git clone ${SPACK_REPO} --branch ${SPACK_REF}
  - . ./spack/share/spack/setup-env.sh
script:
  - spack env activate .
  - spack ci generate
    --spack-repo ${SPACK_REPO} --spack-ref ${SPACK_REF}
    --output-file "${CI_PROJECT_DIR}/jobs_scratch_dir/pipeline.yml"
after_script:
  - rm -rf ./spack
artifacts:
  paths:
    - "${CI_PROJECT_DIR}/jobs_scratch_dir/pipeline.yml"

If the spack ci generate command receives those extra command line arguments, then it adds similar before_script and after_script sections for each of the spack ci rebuild jobs it generates (cloning and sourcing a custom spack in the before_script and removing it again in the after_script). This gives you control over the version of spack used when the rebuild jobs are actually run on the gitlab runner.

Environment variables affecting pipeline operation

Certain secrets and some other information should be provided to the pipeline infrastructure via environment variables, usually for reasons of security, but in some cases to support other pipeline use cases such as PR testing. The environment variables used by the pipeline infrastructure are described here.

AWS_ACCESS_KEY_ID

Needed when binary mirror is an S3 bucket.

AWS_SECRET_ACCESS_KEY

Needed when binary mirror is an S3 bucket.

S3_ENDPOINT_URL

Needed when binary mirror is an S3 bucket that is not on AWS.

CDASH_AUTH_TOKEN

Needed in order to report build groups to CDash.

SPACK_SIGNING_KEY

Needed to sign/verify binary packages from the remote binary mirror.