---

name: labml description: "Guides labml experiment tracking, helper training, remote execution, and app-server workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

LabML

Use this skill for the LabML repository family: experiment tracking, logging, configuration, monitoring, helper training loops, remote job orchestration, and the LabML monitoring app.

Start here

• Read references/package-map.md to choose the right distribution or install set.
• Read references/workflows.md when you need a quick workflow map across the four major subskills.
• Read references/troubleshooting.md when imports, configs, remote jobs, GPU monitoring, or the app server fail.
• Read references/repo-provenance.md when checking whether this skill matches the current checkout or before refreshing it.
• Run scripts/check_labml_stack.py for a quick read-only package and backend health check.

Install

Choose distributions and focused extras from references/package-map.md rather than installing every package. The common distribution set is:

pip install labml labml-helpers labml-remote labml-app

For the helper remote-dataset surface, install its direct-import extra:

pip install 'labml-helpers[remote-dataset]'

The remote-dataset extra covers matplotlib, urllib3, fastapi, and uvicorn imported by labml_helpers.datasets.remote; labml-helpers[plotting] covers the direct matplotlib plotting import. These are focused direct dependencies, not a complete transitive closure or a claim that every workflow is ready. Training and monitoring examples may additionally need torch, torchvision, psutil, and py3nvml.

The app server additionally needs a running MongoDB service, settings, and packaged static frontend assets. If the app backend imports but the server entrypoint fails, read the server sub-skill troubleshooting notes.

Route map

tracking — sub-skills/tracking/SKILL.md

Use this route for:

• experiment, tracker, logger, monit, lab, manage, and AppAPI.
• .labml.yaml configuration files, run metadata, git metadata, and hardware monitoring from the client package.
• labml CLI commands such as capture, launch, monitor, service, and the client-side app-server launcher.
• Experiment logging recipes, dynamic configs, and framework integration notes for PyTorch, Lightning, Keras, FastAI, and analytics examples.

helpers — sub-skills/helpers/SKILL.md

Use this route for:

• labml_helpers training-loop utilities, metrics, datasets, DeviceConfigs, OptimizerConfigs, SeedConfigs, and the Module wrapper.
• MNISTConfigs, CIFAR10Configs, TrainingLoopConfigs, TrainValidConfigs, SimpleTrainValidConfigs, and the remote dataset helpers.
• Small supervised-training recipes, metric wiring, device selection, and dataset-serving patterns.

remote — sub-skills/remote/SKILL.md

Use this route for:

• labml_remote project bootstrap, .remote/configs.yaml, rsync-based sync, job management, and distributed PyTorch launch helpers.
• Remote server setup, package refresh, command execution, and job tailing.
• SSH, key, exclude-list, and remote-environment troubleshooting.

server — sub-skills/server/SKILL.md

Use this route for:

• labml_app server startup, REST analysis endpoints, metrics views, custom metrics, data stores, and route registration.
• MongoDB-backed app runtime, app settings, analysis registries, and the web UI.
• Server-side deployment, reverse proxy, and runtime troubleshooting.

What not here

• Do not use this root skill for general PyTorch model design or package-wide training helpers; route those to helpers.
• Do not use it for SSH orchestration or remote job management details; route those to remote.
• Do not use it for the monitoring app's backend or deployment details; route those to server.
• Do not depend on the original repository checkout at runtime; all reusable guidance is bundled under references/ or scripts/.

Quick chooser

• If the user says "track an experiment", "log metrics", "use labml config", "monitor hardware", or "use AppAPI", start with tracking.
• If the user says "train with helpers", "DeviceConfigs", "TrainValidConfigs", "optimizer configs", or "remote dataset", start with helpers.
• If the user says "set up remote training", "labml_remote", "job-run", or "helper-torch-launch", start with remote.
• If the user says "start the app server", "labml_app", "analysis endpoint", "custom metric", or "MongoDB-backed UI", start with server.