Use /dstack for CLI commands, YAML fields, apply/attach behavior, service URLs,\nand other dstack syntax. This skill explains how to use dstack runs while the\nmodel-serving configuration is still unknown.
Find a working dstack service configuration for the requested model.
\nBefore submitting a service, use a task on real hardware to test the serving\nimage, install/runtime assumptions, model download, cache path, command, port,\nlaunch flags, resources, env vars, backend/fleet choice, and local model\nrequest. Then submit the same configuration as a service and verify the model\nthrough the dstack service URL.
\nPick the offer whose hardware best fits the goal at hand. Only when several offers fit comparably, choose a VM-based backend, an SSH fleet, or a Kubernetes fleet: they support idle instances and/or instance volumes, so later runs reuse the provisioned/idle instance or instance volumes for caching model weights (and possibly other writes), while container-based backends start clean on every run.
\nFetch https://dstack.ai/docs/concepts/backends.md and classify backends\nfrom the fetched document, not from memory.
Check serving-framework sources early enough to choose the image, command,\nlaunch flags, resources, cache paths, request format, and expected model\nbehavior.
\nFor vLLM and SGLang, use these as credible sources:
\nhttps://recipes.vllm.ai/ and\nhttps://recipes.vllm.ai/models.jsonhttps://docs.sglang.io/ (fetch /llms.txt for the page\nindex)https://docs.sglang.io/cookbook/autoregressive/introhttps://github.com/vllm-project/vllm/releases and\nhttps://github.com/sgl-project/sglang/releaseshttps://www.lmsys.org/blog/2026-07-02-agent-assisted-sglang-developmentBefore submitting a service, start a long-lived task:
\ncommands:\n - sleep infinity\nor an equivalent idle command.
\nSubmit the task detached, attach or SSH into it when available, and run commands\ninside the live environment. Test the image, installs, model download and cache\npath, serving command, port, launch flags, local model request, and expected\nmodel behavior.
\nWhen starting a long-running command in the background from a non-interactive\nSSH command, use nohup, redirect stdin from /dev/null, and redirect\nstdout/stderr to a log file so the SSH command returns while the process keeps\nrunning. For example (the command can be any long-running command):
nohup vllm serve ... </dev/null > /tmp/vllm.log 2>&1 &\nIf the image, hardware choice, or major install path changes, submit another\ntask so the changed setup is tested before service verification.
\nDo not move to a service after checking only GPU visibility, imports, logs, or a\nhealth endpoint. Start the server inside the task and send a request that uses\nthe requested model. For a chat or reasoning model, check the response behavior\nthe endpoint is expected to support, such as reasoning output when that model is\nsupposed to expose it.
\nFollow /dstack structured status guidance when polling task or service status.\nAfter requesting a task or service stop before another submission, wait until\nthat run reaches a terminal status. This allows dstack to reuse its instance or\ninstance volumes when available.
Submit the service after the task has verified the configuration: image,\ncommand, port, resources, env vars, cache mounts if used, backend/fleet choice,\nand model request.
\nUse the service as a duplicate check of the same configuration under dstack\nservice runtime. The model request that worked locally in the task must also work\nthrough the dstack service URL.
\nIf service verification fails because the image, install, model download,\ncommand, resources, cache, or model behavior needs to change, go back to a task.\nIf the tested serving setup is still right and only the dstack service\nconfiguration is wrong, fix the configuration and submit the service again.
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