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stroblmeandClaude Opus 5 1a9753fa9d Let a worker say what machine it is

A worker reported its labels and nothing about the machine behind them, so the
engine could route a node to a GPU box but not tell whether that box had a GPU
free. Inventory — cores, GPUs, memory — now arrives with the hello frame, and
the run frame carries back what the engine allocated for that call.

Which is protocol 2 on both ends. GPUs are never probed: asking a vendor tool
would make the one dependency two, so a GPU is what the batch job says it was
given or what --gpus says. A worker that reports nothing still attaches and is
scheduled by its label alone.

Two things a job scheduler needs: --max-idle stops a worker started for one job
rather than letting it hold its allocation to the walltime, and a refusal is now
fatal instead of a reconnect loop that reads as a hang in a job's log.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01A6HeySA27EkGANZN95QySW

2026-08-27 08:29:35 +02:00

fluksio_worker

Let a worker say what machine it is

2026-08-27 08:29:35 +02:00

LICENSE

License the workspace under AGPL-3.0-or-later

2026-08-24 11:53:54 +02:00

pyproject.toml

Let a worker say what machine it is

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README.md

Say the licence on the two pages PyPI will show

2026-08-24 12:51:53 +02:00

README.md

fluksio-worker

Runs Fluksio nodes on a machine the engine cannot reach — a GPU box, a cluster node, anything behind a NAT. It dials out to the engine over one authenticated websocket, so nothing has to be exposed here.

pip install fluksio-worker

fluksio-worker \
  --url wss://api.example.com/api/v1/workers/attach \
  --token "$FLUKSIO_WORKER_TOKEN" \
  --labels gpu,cuda12 \
  --python /opt/torch-venv/bin/python

--python is the interpreter node code runs on, which is how this machine keeps its own wheels without the engine ever installing them. Mint a token from the engine with POST /api/v1/workers/tokens.

Linux and macOS.