---

name: tracking description: "Routes LabML experiment tracking, logging, configuration, monitoring, and AppAPI workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Tracking

Use this subskill for the client-side LabML workflow: recording experiments, tracking metrics, printing logs, configuring runs, monitoring hardware, and using the AppAPI client.

Use this when

• The task mentions experiment, tracker, logger, monit, lab, manage, AppAPI, .labml.yaml, labml monitor, labml service, labml capture, labml launch, or labml app-server.
• The user wants to record a run, inspect training progress, publish metrics to the monitoring app, or inspect hardware usage.
• The user asks how to integrate LabML into PyTorch, Lightning, Keras, FastAI, or similar training code.

Boundaries

Include:

• Experiment lifecycle helpers and config management.
• Metric tracking, log formatting, and monitored loops.
• Client-side hardware monitoring and service setup.
• App API calls from the client package.
• Framework integration recipes that only need the client runtime.

Exclude or route elsewhere:

• Training-loop abstractions, optimizers, datasets, and helper modules → helpers.
• SSH, rsync, and remote job orchestration → remote.
• FastAPI server internals, MongoDB models, and deployment → server.

Read next

• references/api-reference.md for verified signatures, config keys, and CLI commands.
• references/workflows.md for end-to-end recipes and framework integration patterns.
• references/troubleshooting.md for missing config files, app publishing, monitoring, and service failures.
• scripts/tracking_smoke.py for a safe local experiment/checkpoint smoke test.
• scripts/hardware_probe.py for a read-only CPU/GPU and monitor capability check.

Typical routes

Record a run

Choose this route for experiment.create, experiment.record, tracker.save, logger.log, and monit.loop requests. It covers both compact scalar logging and richer tracked outputs.

Configure a project

Choose this route for .labml.yaml, lab.configure, dynamic hyperparameters, run metadata, and git-info questions.

Monitor hardware

Choose this route for labml monitor, labml service, psutil, and optional py3nvml GPU reporting.

Use the app client

Choose this route for AppAPI requests that read runs, analyses, logs, custom metrics, or data stores from a monitoring backend.

Integrate a framework

Choose this route for LabML patterns inside PyTorch, Lightning, Keras, FastAI, or custom training loops that only need the client package.