Labtasker

Labtasker is a small, Python-native task queue for running independent ML inference, evaluation, and experiment jobs in parallel.

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Documentation: https://luocfprime.github.io/labtasker/

LLM Documentation: https://luocfprime.github.io/labtasker/latest/llms.txt

Source Code: https://github.com/luocfprime/labtasker

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Labtasker distributes independent ML jobs across multiple processes and machines. It adds dynamic control, failure recovery, and structured Task records without requiring each project to build its own task system.

The key features are:

• Effortless and flexible parallelism: Run the same Task queue with multiple Workers. Submit new Tasks, change priorities, or cancel Tasks without interrupting the Workers.
• Resumable and failure-resistant experiments: Retry failed Tasks automatically and recover work when a Worker stops. Restart Workers without rerunning completed Tasks. These lifecycle behaviors are covered by unit and end-to-end tests.
• Structured task records: Keep each Task's arguments, metadata, status, errors, and structured result in one place for inspection.
• Easy to adopt and use: Add Labtasker to existing Python code in fewer than 10 lines, or wrap an existing command with no code changes. The API, non-interactive CLI, Agent Skill, and agent-readable documentation allow an agent to operate Labtasker end to end.

[!TIP] Hand Labtasker operations over to your coding agent. Install the bundled Agent Skill, then let your agent handle the Labtasker workflow end to end through its documented interfaces. You only need to tell your agent which Tasks to run and how they should run in parallel.

Installation

Labtasker requires Python 3.11 or newer. Install the complete package for local use:

python -m pip install labtasker

Or add it to a uv project:

uv add labtasker

The labtasker package installs matching Client and Server releases. You can also install labtasker-client and labtasker-server separately when they run in different environments.

Example

Suppose an existing evaluation program accepts a checkpoint, benchmark task, and seed:

python evaluate.py \
  --checkpoint checkpoints/model.pt \
  --task pick-cube \
  --seed 0

Submit each evaluation case as a Labtasker Task:

labtasker task submit \
  --name pick-cube-seed-0 \
  --args '{"checkpoint":"checkpoints/model.pt","task":"pick-cube","seed":0}' \
  --route robotwin

Then run the existing program through a command Worker:

labtasker loop --route robotwin -- \
  python evaluate.py \
    --checkpoint '%{checkpoint}' \
    --task '%{task}' \
    --seed '%{seed}'

Start one Worker process on each GPU you want to use. All Workers claim from the same Queue and process one Task at a time. The route name robotwin labels which Worker implementation can run the submitted Task.

To save evaluation metrics as the Task result, report them from the evaluation program:

import labtasker

# TODO: Replace this with metrics from your actual evaluator.
labtasker.finish(metrics, skip_if_no_labtasker=True)

Inspect progress and results at any time:

labtasker task list
labtasker task list --status succeeded
labtasker task list --status failed

Follow Run your first experiment for a complete tutorial with copyable code and expected results. Use a Python Worker when a model should remain loaded while the Worker processes multiple Tasks.

When to use Labtasker

Labtasker is designed for independent ML jobs such as:

• model inference over prompts, samples, or dataset shards;
• evaluation across checkpoints, benchmark cases, and random seeds;
• generation and ablation experiments across parameter combinations;
• independent data-processing or analysis jobs.

Labtasker becomes useful when several processes share the work, you need to resume after an interruption without rerunning completed jobs, or you need to add, cancel, or reprioritize jobs during a run.

When NOT to use Labtasker

• A simple loop can be sufficient for a small experiment with a few short jobs that can be rerun in full.
• Use a workflow or DAG system when jobs depend on outputs from earlier jobs.
• Use a cluster or resource scheduler when you need to allocate GPUs, start machines, or manage compute capacity.
• Use an artifact store for model checkpoints, generated media, and other large outputs. Labtasker records their paths or URLs, not the files themselves.

Labtasker is deliberately designed to be conceptually simple and easy to hand over to agents.

Documentation

• Documentation overview: choose the right tutorial, guide, or reference page.
• Run your first experiment: submit several cases and process them through one Queue.
• Why Labtasker?: decide whether Labtasker fits your experiment workflow.
• How Labtasker works: understand Tasks, Workers, routes, Queues, retries, and recovery.
• Inference and evaluation patterns: adapt Labtasker to common ML workloads.
• Command Workers and Python Workers: choose how Tasks run.
• CLI reference and Python API reference: check exact interfaces.
• Specification: read the authoritative product and protocol contract.

Labtasker also includes an Agent Skill that helps compatible coding agents submit Tasks, design Workers, inspect progress, and recover failed work through the documented interfaces.

Web UI

Labtasker WebUI is a separately installed browser interface for Labtasker v2. Track Queue progress, filter Tasks, compare result fields in custom columns, and save views for each experiment.

uvx labtasker-webui

Open http://127.0.0.1:8080 and connect to an existing HTTP Server or a running local project. See Use the Web UI for connection steps and Task controls.

Development

uv sync --all-packages --group dev --frozen
uv run pytest
uv run zensical build --clean

See Development for repository boundaries and the full validation commands.

License

Apache-2.0.