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Version: 2.22 (prerelease)

Environments: Cross-Platform or Remote Builds


Environments are currently in preview, and have not yet stabilized.

We'd love your feedback on how Environments could be most useful to you! Please refer to the tracking issue for known stabilization blockers.

By default, Pants will execute all sandboxed build work directly on localhost. But defining and using additional "environments" for particular targets allows Pants to transparently execute some or all of your build either:

  1. locally in Docker containers
  2. remotely via remote execution
  3. locally, but with a non-default set of environment variables and settings (such as when different platforms need different values, or when cross-building)
  4. locally, but with execution performed in the workspace / repository and not an execution sandbox (with the trade-off that you must be cognizant of build reproducibility)

Defining environments

Environments are defined using environment targets:

  • local_environment - Runs without containerization on localhost (which is also the default if no environment targets are defined).
  • docker_environment - Runs in a cached container using the specified Docker image using a local installation of Docker. If the image does not already exist locally, it will be pulled.
  • remote_environment - Runs in a remote worker via remote execution (possibly with containerization, depending on the server implementation).

Give your environment targets short, descriptive names using the [environments-previews.names] option (usually defined in pants.toml), which consuming targets use to refer to them in BUILD files. That might look like a pants.toml section and BUILD file (at the root of the repository in this case) containing:

linux = "//:local_linux"
linux_docker = "//:local_busybox"
Environment targets and macros

Environment targets are loaded before regular targets in a bootstrap phase, during which macros are unavailable. As such any required field values must be fully defined in the BUILD file without referencing any macros. For optional fields, the use of macros are still discouraged as it may or may not work and Pants makes no guarantees that it will not break in a future version if it were to currently work.

Environment-aware options

Environment targets have fields (target arguments) which correspond to options which are marked "environment-aware". When an option is environment-aware, the value of the option that will be used in an environment can be overridden by setting the corresponding field value on the associated environment target. If an environment target does not set a value, it defaults to the value which is set globally via options values.

For example, the [python-bootstrap].search_path option is environment-aware, which is indicated in its help. It can be overridden for a particular environment by a corresponding environment target field, such as the one on local_environment.

See an option which should be environment-aware, but isn't?

Environments are a new concept: if you see an option value which should be marked environment-aware but isn't, please definitely file an issue!

Consuming environments

To declare which environment they should build with, many target types (but particularly "root" targets like tests or binaries) have an environment= field: for example, python_tests(environment=..).

The environment= field may either:

  1. refer to an environment by name
  2. use one of the following special environment names to select a matching environment: (see "Environment matching" below)
  3. __local__ resolves to any matching local_environment
  4. __local_workspace__ resolves to any matching experimental_workspace_environment
Environment compatibility

Currently, there is no static validation that a target's environment is compatible with its dependencies' environments -- only the implicit validation of the goals that you run successfully against those targets (check, lint, test, package, etc).

As we gain more experience with how environments are used in the wild, it's possible that more static validation can be added: your feedback would be very welcome!

Setting the environment on many targets at once

To use an environment everywhere in your repository (or only within a particular subdirectory, or with a particular target type), you can use the __defaults__ builtin. For example, to use an environment named my_default_environment globally by default, you would add the following to a BUILD file at the root of the repository:


... and individual targets could override the default as needed.

Building one target in multiple environments

If a target will always need to be built in multiple environments (rather than conditionally based on which user is building it: see the "Toggle use of an environment for some consumers" section), then you can use the parametrize builtin for the environment= field. If you had two environments named linux and macos, that would look like:

environment=parametrize("linux", "macos"),

Environment matching

A single environment name may end up referring to different environment targets on different physical machines, or with different global settings applied: this is known as environment "matching".

  • local_environment targets will match if their compatible_platforms= field matches localhost's platform.
  • docker_environment targets will match if Docker is enabled, and if their platform= field is compatible with localhost's platform.
  • remote_environment targets will match if Remote execution is enabled.
  • experimental_workspace_environment targets will match if their compatible_platforms= field matches localhost's platform.

If a particular environment target doesn't match (other than for workspace_enviroment targets), it can configure a fallback_environment= which will be attempted next. This allows for forming preference chains which are referred to by whichever environment name is at the head of the chain. This does not apply to workspace_enviroment targets because in-workspace execution differs significantly from the execution in the other environments due to the lack of an execution sandbox.

For example: a chain like "prefer remote execution if enabled, but fall back to local execution if the platform matches, otherwise use docker" might be configured via the targets:




In future versions, environment targets will gain additional predicates to control whether they match (for example: local_environment will likely gain a predicate that looks for the presence or value of an environment variable. But in the meantime, it's possible to override which environments are matched for particular use cases by overriding their configured names: see the "Toggle use of an environment" workflow below for an example.

Example workflows

Enabling remote execution globally

remote_environment targets match unless the --remote-execution option is disabled. So to cause a particular environment name to use remote execution whenever it is enabled, you could define environment targets which try remote execution first, and then fall back to local execution:



You'd then give your remote_environment target an unassuming name like "default":

default = "//:remote_busybox"
local = "//:local"

... and use that environment by default with all targets. Users or consumers like CI could then toggle whether remote execution is used by setting --remote-execution.

Speculation of remote execution

The 2.15.x series of Pants does not yet support "speculating" remote execution by racing it against another environment (usually local or docker). While we expect that this will be necessary to make remote execution a viable option for local execution on user's laptops (where network connections are less reliable), it is less critical for CI use-cases.

Use a docker_environment to build the inputs to a docker_image

To build a docker_image target containing a pex_binary which uses native (i.e. compiled) dependencies on a macOS machine, you can configure the pex_binary to be built in a docker_environment.

You'll need a docker_environment which uses an image containing the relevant build-time requirements of your PEX. At a minimum, you'll need Python itself:


Next, mark your pex_binary target with this environment (with the name python_bullseye: see "Defining environments" above), and define a docker_image target depending on it.


"FROM python:3.9.14-slim-bullseye@sha256:abcd123...",
"ENTRYPOINT ["/main"]",
"COPY examples/main.pex /main",
Compatibility of docker_environment and docker_image

Note that the Docker image used in your docker_environment does not need to match the base image of the docker_image targets that consume them: they only need to be compatible. This is because execution of build steps in a docker_environment occurs in an anonymous container, and only the required inputs are provided to the docker_image build.

This means that your docker_environment can include things like compilers or other tools relevant to your build, without needing to manually use multi-stage Docker builds.

Toggle use of an environment for some consumers

As mentioned above in "Environment matching", environment targets "match" based on their field values and global options. But if two environment targets would be ambiguous in some cases, or if you'd otherwise like to control what a particular environment name means (in CI, for example), you can override an environment name via options.

For example: if you'd like to use a particular macOS environment target locally, but override it for a particular use case in CI, you'd start by defining two local_environment targets which would usually match ambiguously:



... and then assign one of them a (generic) environment name in pants.toml:

macos = "//:macos_laptop"

You could then override that name definition in (note the use of the .add suffix, in order to preserve any other named environments):
macos = "//:macos_ci"

In-Workspace Execution (experimental_workspace_environment)

The workspace_enviroment target type configures a special "workspace" environment in which build actions are invoked in the repository / workspace instead of an execution sandbox as would be done with local_environment executions.

The primary motivation for this feature is to better support integration with third-party build orchestration tools (for example, Bazel) which may not operate properly when not invoked in the repository (including in some cases incurring signifcant performance penalties).

Caching and reproducibility

Pants' caching relies on all process being reproducible based solely on inputs in the repository. Processes executed in a workspace environment can easily accidentally read unexpected files, that aren't specified as a dependency. Thus, Pants puts that burden on you, the Pants user, to ensure a process output only depends on its specified input files, and doesn't read anything else.

If a process isn't reproducible, re-running a build from the same source code could fail unexpectedly, or give different output to an earlier build.

The special environment name __local_workspace__ can be used to select a matching experimental_workspace_environment based on its compatible_platforms attribute.

There is no fallback_environment= atribute on workspace_enviroment targets because in-workspace execution differs significantly from the other environemnts due to the lack of an execution sandbox.