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Using Pants in CI

Suggestions for how to use Pants to speed up your CI (continuous integration).

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Example .travis.yml

See https://github.com/pantsbuild/example-python/blob/master/.travis.yml for an example of how to configure Pants with Travis CI.

pants.ci.toml

We recommend creating a pants.ci.toml config file with options specific to your CI setup.

[GLOBAL]
# The Pants daemon is not very useful in CI, given that it
# will be restarted every run.
pantsd = false

dynamic_ui = false
# Ensure colors are used (if your CI provider supports it). 
colors = true

# Limit the maximum number of concurrent processes. Change this
# to a number that makes sense for your CI setup, based on 
# the number of cores/threads.
process_execution_local_parallelism = 4  

[python-setup]
# Limit the maximum number of concurrent jobs used to resolve third
# party dependencies. The max level of parallelism will be
# `process_execution_local_parallelism x resolver_jobs`, but 
# often you will have one resolve process at a time.
resolver_jobs = 2

Then, in your CI script or config, set the environment variable PANTS_CONFIG_FILES to use this new config file, in addition to the default pants.toml. For example:

export PANTS_CONFIG_FILES=pants.ci.toml

# Then, your normal CI setup
./pants test ::
...

Directories to cache

In your CI's config file, we recommend caching these directories:

  • $HOME/.cache/pants/setup: the initial bootstrapping of Pants.
  • $HOME/.cache/pants/named_caches: caches of tools like pip and PEX.
  • $HOME/.cache/pants/lmdb_store: cached content for prior Pants runs.

See Troubleshooting for how to change these cache locations.

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Nuking the cache when too big

In CI, the cache must be uploaded and downloaded every run. This takes time, so there is a tradeoff where too large of a cache will slow down your CI.

You can use this script to nuke the cache when it gets too big:

function nuke_if_too_big() {
  path=$1
  limit_mb=$2
  size_mb=$(du -m -d0 ${path} | cut -f 1)
  if (( ${size_mb} > ${limit_mb} )); then
    echo "${path} is too large (${size_mb}mb), nuking it."
    rm -rf ${path}
  fi
}

nuke_if_too_big ~/.cache/pants/lmdb_store 2048
nuke_if_too_big ~/.cache/pants/setup 768
nuke_if_too_big ~/.cache/pants/named_caches 1024

Recommended commands

Approach #1: only run over changes files

Because Pants understands the dependencies of your code, you can use Pants to speed up your CI by only running tests and linters over files that actually made changes.

We recommend running these commands in CI:

$ ./pants --version  # This will bootstrap Pants
$ ./pants --changed-since=origin/main lint
$ ./pants \
  --changed-since=origin/main \
  --changed-dependees=transitive \
  typecheck test

Because most linters do not care about a target's dependencies, we lint all changed targets, but not any dependees of those changed targets.

Meanwhile, tests should be rerun when any changes are made to the tests or to dependencies of those tests, so we use the option --changed-dependees=transitive. typecheck should also run on any transitive changes.

See Advanced target selection for more information on --changed-since and alternative techniques to select targets to run in CI.

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This will not handle all cases, like hooking up a new linter

For example, if you add a new plugin to Flake8, Pants will still only run over changed files, meaning you may miss some new lint issues.

For absolute correctness, you may want to use Approach #2. Alternatively, add conditional logic to your CI, e.g. that any changes to pants.toml trigger using Approach #2.

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GitHub Actions: use Checkout

To use --changed-since, you may want to use the Checkout action.

By default, Checkout will only fetch the latest commit, so you should set fetch-depth: 0.

Approach #2: run over everything

Alternatively, you can simply run over all your code. Pants's caching means that you will not need to rerun on changed files.

$ ./pants --version  # This will bootstrap Pants
$ ./pants lint typecheck test ::

However, when the cache gets too big, it should be nuked (see "Directories to cache"), so your CI may end up doing more work than Approach #1.


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