xpm-mlboard

Lightweight experimaestro services to monitor ML learning curves during experiments. It launches a visualization backend and aggregates the run directories produced by your tasks so you can watch training live.

It is intentionally torch-free: the package only depends on experimaestro, with the heavier visualization tools pulled in as optional extras.

Backends

• TensorBoard (xpm_mlboard.TensorboardService) — runs tensorboard as an isolated subprocess on a free port and symlinks each task's tagged run directory into a single runs/ folder.

The xpm_mlboard.MonitoringService / xpm_mlboard.SymlinkMonitoringService base classes make it straightforward to add other backends (e.g. Weights & Biases).

Installation

As a project dependency (with the TensorBoard backend):

uv add "xpm-mlboard[tensorboard]"

Adding the plugin to experimaestro installed as a uv tool

If you run experimaestro as a uv tool, inject this plugin into the tool's environment with --with (re-run the install to add the plugin to the existing tool):

uv tool install experimaestro --with "xpm-mlboard[tensorboard]"

Usage

Add the service to your experiment and register each task's run directory:

from xpm_mlboard import TensorboardService

# `xp` is the experimaestro experiment
service = xp.add_service(TensorboardService(xp.resultspath / "runs"))

# When you submit a task, register its run directory so it shows up:
task = MyLearningTask(...).submit()
service.add(task, task.logpath)

Wiring the service into an experiment is typically done through a project-specific experiment helper (for instance xpm_torch.experiments.LearningExperimentHelper, which exposes helper.monitoring_service).

Custom backends

Subclass SymlinkMonitoringService (filesystem-based backends) or MonitoringService (anything else) and implement the backend-specific bits:

from xpm_mlboard import SymlinkMonitoringService
from experimaestro.scheduler.services import ProcessWebService


class MyBackendService(SymlinkMonitoringService, ProcessWebService):
    id = "mybackend"

    def description(self):
        return "My backend service"

    def _build_command(self):
        ...

    def _wait_for_ready(self):
        ...

License

GPL-3