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By default, all BuildBuddy executors are placed in a single pool - and any task can run on any executor (running the same operating system and cpu architecture). Platforms can be used to specify custom Docker images in which to run your actions, but sometimes you want control over more properties of the executor machine - like available memory, access to GPUs, or physical location. To support these use cases, BuildBuddy allows executors to be registered in different pools - and for Bazel to select from these pools at either the Platform level or the target level, depending on your needs.

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Deploying executors in a pool

When creating an executor deployment, you can specify the name of the pool its executors should be registered to with the MY_POOL environment variable. This can be set to any string value.
If using the buildbuddy/buildbuddy-executor Helm charts, you can set this using the poolName value.

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Setting the app’s default pool name

By default, both executors and the BuildBuddy app do not set a pool name and any RBE request that comes in without a Pool property set will be sent to the default pool. If you’d like requests without a Pool property to be sent to a different default pool, you can set the app’s default_pool_name in the remote_execution block of its config.yaml.
config.yaml
remote_execution:
  enable_remote_exec: true
  default_pool_name: my-default-pool

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Selecting a pool to run your builds on

Now that you’ve deployed multiple executor pools, you can select which pool you’d like your builds to run on - either at the platform level or the target level.
You can configure BuildBuddy RBE to use a custom executor pool at the platform level, by adding the following rule to a BUILD file:
BUILD
platform(
    name = "gpu_platform",
    exec_properties = {
        "OSFamily": "Linux",
        "Pool": "my-gpu-pool",
    },
)
Make sure to replace my-gpu-pool with your pool name.You can then pass this configuration to BuildBuddy RBE with the following flag:
--host_platform=//:gpu_platform
This assumes you’ve placed this rule in your root BUILD file. If you place it elsewhere, make sure to update the path accordingly.

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Common use cases

GPU Workloads

Deploy a pool with GPU-enabled machines for ML training or compute-intensive tasks.

High Memory

Create a pool with high-memory executors for memory-intensive build and test targets.

Geographic Location

Deploy pools in specific regions to minimize latency or comply with data residency requirements.

Specialized Hardware

Use pools with specific CPU architectures (ARM64, x86_64) or other hardware requirements.

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