Manage GPU compute jobs on the Qizhi platform with kubectl-style commands.
npx skills add https://github.com/lightrain-a/medtrace-aris --skill qzcli-lightrain-a
Please help me install this Agent Skill. Skill: qzcli Source: https://github.com/lightrain-a/medtrace-aris/tree/main/.vendor/aris/skills/qzcli Command: npx skills add https://github.com/lightrain-a/medtrace-aris --skill qzcli-lightrain-a
qzcli helps you manage GPU compute jobs on the Qizhi (启智) platform without manually navigating web consoles or tracking job details by hand.
Run qzcli login, then discover resources with qzcli res -u, and finally list running jobs with qzcli ls -c --all-ws.
High-intent search queries and answers about installing and using this skill.
You can manage Qizhi GPU jobs without the web console by using kubectl-style CLI commands to submit, monitor, and stop distributed training tasks. It provides terminal-based lifecycle control for compute jobs across workspaces.
To submit distributed training jobs in batch, you can use a JSON configuration file with default parameters and matrix-style command templating. This allows you to create multiple GPU compute jobs simultaneously.
You can monitor GPU utilization across workspaces by listing jobs and checking status with specific CLI flags. Commands allow you to watch updates and view workspace or project GPU utilization directly.
No, the Qizhi CLI does not require manual workspace and compute group selection. It uses cached resource discovery to automatically resolve workspace and compute group identifiers before submitting work.
You authenticate to the Qizhi platform via CLI by running the login command. It supports authentication through CLI input, environment variables, .env files, and config cookie persistence.
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