Physical resources

These are advanced features, which require some familiarity with Linux process management to use well. For this reason, this documentation is intentionally sparse. You will want to review the code in bistro/physical when enabling these features.

The mentions of physical resources in RemoteWorkerRunner.cpp are responsible for integrating the capability to query resources with actual scheduling and enforcement.

Please talk to us if you need this documentation to be fleshed out.

In principle, it would be desirable to extend this mechanism to support automatic discovery of other resource types — including data resources. If you are looking to do this work, please open an issue to discuss the design.

Discovering worker host physical resources

To handle worker host pools with heterogeneous hardware, Bistro can automatically discover — and convert into its own internal logical resource slots:

  • System CPU cores
  • System RAM
  • nVidia GPUs on the system, including the specific GPU type

Discovering available physical resources on a host is tricky business. You cannot just read /proc/cpuinfo and run with it. Linux provides cgroup and cgroup-v2 for restricting the resources available to a process tree – and it is completely reasonable for bistro_worker to be sandboxed in this way. At present, Bistro’s code for discovering usable physical resources is aware of neither cgroup nor cgroup-v2. However, proper cgroup support would be better – patches are welcome, see bistro/physical. For one workaround, see physical_reserve_amount below.

On the plus side, besides auto-detecting system memory & CPU cores, Bistro knows how to query available nVidia GPUs via nvidia-smi. Patches for other resource types are also welcome.

Here is an example configuration for automatically setting 3 resources on all worker-level nodes:

"physical_resources": {
    "ram_mb": {
        "logical_resource": "ram_gb",
        "multiply_logical_by": 1024,
        "physical_reserve_amount": 4096
    "cpu_core": {
        "logical_resource": "cpu_core",
        "enforcement": "none"
    "gpu_card": {
        "logical_resource": "gpu"

If your worker level defines logical resource names of the form GPU: [Card Name] (with the card name as returned by nvidia-smi’s query field name), the above incantation will also automatically populate those resources on your workers.

physical_reserve_amount is subtracted from the system’s resources before converting physical resources to units of logical slots. This serves to reserve a fixed part of the system’s resources for non-Bistro services running on the system.

Enforcing worker resource consumption

A task may declare it needs 2 GB of RAM and 1 core, but in actuality could use 10 cores and 20GB of RAM. In a shared compute cluster, this can lead to instability and poor performance.

Bistro can use Linux cgroup subsystems (when appropriately configured, see Supervising and killing tasks) to enforce CPU & RAM utilization.

At present, cgroup-v2 is not supported.

In the previous section’s physical_resources example, you will note that cpu_core has enforcement set to none. That demonstrates the syntax for configuring task resource enforcement.

Bistro has exactly two options:

  • RAM enforcement can be none or hard. In the latter case, the task’s memory subsystem cgroup will be limited to the amount of physical RAM that corresponds to the task’s declared logical resource requirements.

  • CPU enforcement can be none or soft. In the latter case, the task’s cpu subsystem cgroup will have cpu.shares set in proportion to the the task’s declared logical resource requirements. This simply assures that — in case of CPU contention — the CPU resources allotted to Bistro’s cgroup slice are distributed among the Bistro tasks in proportion to their logical CPU resource specifications.

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