Manage GPU compute jobs on the Qizhi platform via command-line interface.
npx skills add https://github.com/dogekiki/SP-test --skill qzcli-dogekiki
Please help me install this Agent Skill. Skill: qzcli Source: https://github.com/dogekiki/SP-test/tree/main/.trae/skills/qzcli Command: npx skills add https://github.com/dogekiki/SP-test --skill qzcli-dogekiki
This skill simplifies the complex process of managing GPU compute jobs on the Qizhi platform by providing a unified, kubectl-style interface that replaces manual web-portal interactions.
Use the qzcli skill to list all currently running jobs in your active workspace.
High-intent search queries and answers about installing and using this skill.
You can manage distributed training jobs on Kubernetes by using a unified command-line interface to submit, monitor, and terminate tasks directly from your terminal, bypassing manual web portal interactions entirely.
Submit GPU compute jobs from the terminal by executing template-based batch submission configurations through a kubectl-style interface, facilitating large-scale training experiments and workspace discovery.
Yes, monitoring GPU jobs requires local configuration of the command-line toolchain and authentication via credentials to successfully interact with the platform API and view real-time job statuses.
Yes, you can execute large-scale training experiments through command-line job scheduling by utilizing template-based batch submission configurations to manage distributed training tasks efficiently.
The best way to discover compute groups and hardware specifications is using command-line resource discovery, which automatically caches and queries available workspaces and hardware data for your distributed training jobs.
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