Can I use this for non-ML tasks?

While primarily designed for GPU-intensive experiments, its robust SSH and task queuing capabilities can be adapted for any remote batch processing workflow.

Does this skill support remote GPU clusters?

Yes, it includes built-in tools for SSH execution, allowing it to upload files, download results, and run commands on remote servers.

What is 'Aggressive Mode' in this skill?

Aggressive mode is a configuration setting that allows the supervisor to use more intensive scheduling strategies based on hardware availability percentages.

How does it manage GPU resources?

You can set specific GPU memory thresholds and polling intervals. The supervisor only dispatches new experiments when the hardware meets your criteria.

Sibyl Experiment Supervisor

Name: Sibyl Experiment Supervisor
Author: qcai0427

byqcai0427

0•

데이터 과학 및 ML

Orchestrates machine learning experiments by monitoring GPU availability and dynamically dispatching tasks to remote infrastructure.

The Sibyl Experiment Supervisor is a specialized orchestration agent designed to manage the complexities of long-running machine learning workflows. It automates the entire experiment lifecycle by continuously polling GPU status, managing task queues, and correcting runtime state drift. By utilizing SSH-based remote server integration, it allows developers to maximize hardware utilization through intelligent dispatching, configurable resource thresholds, and automated environment synchronization, ensuring that experiments run efficiently without manual oversight.

주요 기능

01Automated management of experiment queues and task IDs

02Configurable aggressive scheduling modes for maximum hardware throughput

03Real-time GPU status monitoring and idle detection

04Dynamic task dispatching across local and remote SSH servers

050 GitHub stars

06Intelligent handling of runtime state and status drift

사용 사례

01Monitoring and recovering long-running experiments that experience connection or state issues

02Managing large-scale hyperparameter tuning queues with automated resource allocation

03Automating deep learning training jobs across a cluster of remote GPU servers

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add qcai0427/sibyl-codex sibyl-experiment-supervisor

For use in Claude.ai and ChatGPT

주요 기능

01Automated management of experiment queues and task IDs

02Configurable aggressive scheduling modes for maximum hardware throughput

03Real-time GPU status monitoring and idle detection

04Dynamic task dispatching across local and remote SSH servers

050 GitHub stars

06Intelligent handling of runtime state and status drift

사용 사례

01Monitoring and recovering long-running experiments that experience connection or state issues

02Managing large-scale hyperparameter tuning queues with automated resource allocation

03Automating deep learning training jobs across a cluster of remote GPU servers

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add qcai0427/sibyl-codex sibyl-experiment-supervisor

For use in Claude.ai and ChatGPT