Make your ML experiment wrapper scripts smarter with...
Install • Tutorial / Demo • Documentation • FAQs • Releases
🌟 Labtasker makes ML experiment wrapper scripts smarter with task prioritization,
failure handling, halfway resume and more: just change 1 line of code.
If you like our project, please give us a star ⭐ on GitHub for latest update.
✨ When and Where to Use
TLDR: Replace for loops in your experiment wrapper script with labtasker to enable features like experiment
parallelization, dynamic task prioritization, failure handling, halfway resume, and more.
🐳 For detailed examples and concepts, check out the documentation.
🧪️ A Quick Demo
This demo shows how to easily submit task arguments and run jobs in parallel.
It also features an event listener to monitor task execution in real-time and automate workflows,
such as sending emails on task failure.
For more detailed steps, please refer to the content in
the Tutorial / Demo.
⚡️ Features
- ⚙️ Easy configuration and setup.
- 🧩 Versatile and minimalistic design.
- 🔄 Supports both CLI and Python API for task scheduling.
- 🔌 Customizable plugin system.
🔮 Supercharge Your ML Experiments with Labtasker
- ⚡️ Effortless Parallelization: Distribute tasks across multiple GPU workers with just a few lines of code.
- 🛡️ Intelligent Failure Management: Automatically capture exceptions, retry failed tasks, and maintain detailed
error logs.
- 🔄 Seamless Recovery: Resume failed experiments with a single command - no more scavenging through logs or
directories.
- 🎯 Real-time Prioritization: Changed your mind about experiment settings? Instantly cancel, add, or reschedule
tasks without disrupting existing ones.
- 🤖 Workflow Automation: Set up smart event triggers for email notifications or task workflow based on FSM
transition events.
- 📊 Streamlined Logging: All stdout/stderr automatically organized in
.labtasker/logs - zero configuration
required.
- 🧩 Extensible Plugin System: Create custom command combinations or leverage community plugins to extend
functionality.
- 🦾 AI Agent Skills: First-class skill definitions for Claude Code and OpenCode — let your AI assistant decompose and manage experiment scripts automatically.
🛠️ Installation
[!NOTE]
You need a running Labtasker server to use the client tools.
Simply use the installed Python CLI labtasker-server serve or use docker-compose to deploy the server.
See deployment instructions.
1. Install via PyPI
# Install with optional bundled plugins
pip install 'labtasker[plugins]'
2. Install the Latest Version from GitHub
pip install git+https://github.com/luocfprime/labtasker.git
🚀 Quick Start
Use the following command to launch a labtasker server in the background:
labtasker-server serve &
Use the following command to quickly setup a labtasker queue for your project:
labtasker init
Then, use labtasker submit to submit tasks and use labtasker loop to run tasks across any number of workers.
[!TIP]
Use AI to help decompose your experiment scripts. Install the Labtasker skill for your agent:
Claude Code — install via marketplace:
/plugin marketplace add luocfprime/labtasker
/plugin install labtasker-skill@labtasker
Or other agents — install via CLI:
npx skills add luocfprime/labtasker
Or copy skills/labtasker/SKILL.md to ~/.claude/skills/labtasker/SKILL.md
📚 Documentation
