You have a couple of boxes with GPUs — a workstation, a server, maybe a laptop — wired together over Tailscale or a LAN. You want to fire off training runs, data pipelines, and long batch jobs from anywhere, have them land on whichever machine actually has the VRAM free, survive across sessions, and get preempted cleanly when something more important shows up. You don't have a cloud, a Kubernetes cluster, or a Slurm install, and you don't want one.

jobd is that missing piece: a small broker that turns a handful of personal machines into a single queue — and an LLM agent can drive it directly.

# from any machine on your tailnet:
job submit --project myproj --gpu --vram-required 16 --wait -- python train.py
# → routed to whichever worker has ≥16 GB VRAM free, streamed back to your terminal

Why it exists

Most schedulers assume a datacenter. The lightweight ones that don't (a bare nohup, a tmux session, an ssh-and-pray script) give you nothing: no queue, no VRAM-aware routing, no preemption, no record of what ran where. jobd fills the gap between "ssh in and run it" and "stand up Slurm":

• VRAM-fit routing. The broker matches each job against live worker capacity (free VRAM / RAM / CPUs, capability tags, arch/OS) and dispatches to a worker that actually fits — instead of you guessing which box is free.
• Preempt + checkpoint. A higher-priority job can preempt a running one: the worker sends SIGTERM, the workload gets a grace window to checkpoint, then SIGKILL. A preempted job reaches a terminal preempted state with a durable checkpoint to resume from — it isn't silently re-run. (See docs/preemption.md.)
• Survives sessions. Submit, close your laptop, check back tomorrow. Jobs live in the broker, not your shell.
• Agent-native. Ships a first-class MCP server so an LLM agent (Claude Code, etc.) can submit, monitor, and babysit jobs as tool calls — the thing most schedulers bolt on as an afterthought, if at all.
• Yours. One broker process you run on a machine you own. No accounts, no egress, no per-GPU-hour billing. Tailnet-bound by default.

Why not just use…?

Closest in spirit are Pueue and task-spooler (single-machine by design) and HyperQueue (multi-machine, HPC-shaped). jobd's niche is the 2–5-GPU homelab: multi-machine live VRAM-fit routing + preempt/checkpoint + a native MCP interface — a combination none of the above offers — with nothing heavier than a Python process per host.

Architecture

flowchart TD
    CLI["job CLI"]:::client --> B
    MCP["jobd-mcp<br/>MCP tools"]:::client --> B
    API["HTTP · SSE"]:::client --> B
    B["<b>jobd broker</b> — FastAPI<br/>queue · matcher · priorities · SQLite"]:::broker
    B <-->|poll · dispatch| WA["worker A<br/>24 GB GPU"]:::worker
    B <-->|poll · dispatch| WB["worker B<br/>8 GB GPU"]:::worker
    B <-->|poll · dispatch| WC["worker C<br/>CPU-only"]:::worker
    classDef client fill:#1f2937,stroke:#4b5563,color:#e5e7eb;
    classDef broker fill:#0e7490,stroke:#155e75,color:#ecfeff;
    classDef worker fill:#14532d,stroke:#166534,color:#dcfce7;

Workers poll the broker (pull model — no inbound connection to a worker); the broker matches each job against live capacity and hands it back on the poll. One broker process, one poller per host.

• Broker — a FastAPI + SQLite service. Holds the queue, runs the matcher, resolves per-project priorities and defaults, exposes a small HTTP API and an SSE stream. Single source of truth.
• Workers — lightweight polling agents, one per host. Each advertises live capacity via heartbeat, claims jobs it can run, executes them (shell=False, no shell-injection surface), streams logs back, and honors preemption signals.
• Clients — the job CLI, the jobd-mcp MCP server, or anything that speaks the HTTP API.

Install

pip install jobd               # broker + CLI
pip install "jobd[mcp]"        # adds the MCP server
pip install "jobd[worker]"     # adds the worker daemon (jobd-worker)

Requires Python ≥ 3.11. Everything ships in the one jobd package: the broker (jobd), the CLI (job), the MCP server (jobd-mcp), and the worker (jobd-worker). The worker's runtime deps (httpx, psutil, pyyaml, nvidia-ml-py) live behind the [worker] extra since they're only needed on machines that actually run jobs. scripts/install-worker.sh sets a worker up under ~/jobd-worker with its own venv and a generated config.

Quickstart (single host)

# 1. start the broker (binds 127.0.0.1:8765 by default)
JOBD_ALLOW_NO_AUTH=1 jobd          # no-auth is fine for a loopback-only broker

# 2. in another shell, install + start a worker pointed at it
pip install "jobd[worker]"
JOBD_URL=http://127.0.0.1:8765 JOBD_WORKER_HOST=local jobd-worker

# 3. submit a job and wait for it
job submit --project demo --wait -- echo hello
job list
job logs <id>

For a real multi-host deployment (Docker broker + systemd workers, Tailscale binding, shared auth token), see docs/security.md and the templates in docker-compose.yml and scripts/. Adding a worker to a running fleet is one command:

job fleet add user@newbox      # ssh in, install pinned to the broker's version,
                               # wire systemd units + the self-update timer,
                               # verify it registers. `job fleet status` shows drift.