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gpuqu

PyPIInfrastructure

PulseGate's liveness check found it on 3 Oct 2026; it is registered on GitHub and PyPI and has been in the index since 15 Aug 2026. How this is checked

gpuqu is an MIT-licensed cross-platform GPU job queue for scheduling GPU workloads such as training jobs. It includes a web UI and metrics dashboard and is distributed as a Python package with no runtime pip dependencies.

Inferred · not functionally tested

Open SourceMITCLIWebSelf-hosted
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Overview

6 features

Purpose: Managing and monitoring queued GPU workloads without building a scheduler and dashboard from scratch.

Inferred · not functionally tested

Audience: ML engineers and developers managing GPU workloads

Inferred · not functionally tested

Functions: monitoring

Inferred · not functionally tested

Interfaces: API: unknown · MCP: unknown · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)

Recorded constraints: pricing: open_source · license: MIT · platforms: CLI · deployment: cli, browser, self_hosted

Constraint provenance is unknown; confirm requirements with the publisher.

Record sources: pypi.org · github.com. These links do not verify the individual claims.

In the Other infrastructure space, gpuqu takes a focused approach. Inferred · not functionally tested: It focuses on managing and monitoring queued GPU workloads without building a scheduler and dashboard from scratch. Inferred · not functionally tested: gpuqu is an open-source project aimed at ML engineers and developers managing GPU workloads. Basis unknown · not verified: gpuqu is open source under the MIT license. Basis unknown · not verified: It ships for the command line and the web, and it can be self-hosted.

Behind gpuqu is Weidows, and it first shipped in 2026. Development happens publicly on GitHub with 11 commits in the last 90 days. Inferred · not functionally tested: Among its 6 catalogued features are GPU job queue, job scheduling, and Web UI.

Summary written by a language model from the project’s public pages.

Tasks: Inferred · not functionally tested

  • GPU job queue
  • Job scheduling
  • Web UI
  • Metrics dashboard
  • Cross-platform support
  • Zero dependencies

Topics: Inferred · not functionally tested

Tags
gpu-schedulingjob-queuenvidia-workloadstraining-jobsmetrics-dashboard

JSON profile · Text profile · Access guide

Built with & integrations

Runs on
CLIBrowserSelf-hosted

Trust & compliance

License
MIT
Public signals
HTTPSOpen SourceFree tierGitHubActive maintenance

Indexing history

1

What PulseGate has recorded for this listing

  1. Indexed15 Aug · 21:30 UTC
    gpuqu seen via PyPI Bulk Enumerator
    Source: PyPI Bulk Enumerator · Open

Frequently asked questions about gpuqu

What is gpuqu?
Inferred · not functionally tested: Gpuqu focuses on managing and monitoring queued GPU workloads without building a scheduler and dashboard from scratch. It is catalogued under Other infrastructure on PulseGate.
Who should use gpuqu?
Inferred · not functionally tested: gpuqu is an open-source project built for ML engineers and developers managing GPU workloads.
Is gpuqu free?
Basis unknown · not verified: Yes — gpuqu is open source under the MIT license and free to use.
What platforms does gpuqu run on?
Basis unknown · not verified: gpuqu runs on the command line and the web. It can also be self-hosted.
Is gpuqu still active?
PulseGate's liveness check found it on 3 Oct 2026. Its GitHub repository shows 11 commits in the last 90 days.
What are alternatives to gpuqu?
Similar projects tracked by PulseGate include gpuhire, gq-local, and gpusched.gpuhiregq-localgpusched
Who makes gpuqu?
gpuqu is developed by Weidows.
How long has gpuqu been around?
gpuqu first shipped in 2026.

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