app/docs/code/workers.md

# Remote workers

The engine runs where the automations are. The GPU is somewhere else, the
Raspberry Pi with the relays is in a shed, and neither of them is on the same
network as the other.

A **worker** is a process that runs the code of nodes marked for it. It dials
*out* to the engine over one authenticated websocket, so nothing on that
machine has to be reachable, and nothing has to expose the engine's state
backend across hosts, which it never should.

## Install and attach

```sh
pip install fluksio-worker

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

`fluksio-worker` is its own distribution: the agent, the node runner, and
`websockets`. Nothing of the engine, so a GPU box does not install a database
driver in order to run a training step. An engine host already has it, and
`fluksio worker …` is the same program.

| Option | Default | What it does |
|---|---|---|
| `--url` | **required** | `wss://…/api/v1/workers/attach` |
| `--token` | `$FLUKSIO_WORKER_TOKEN` | the credential, minted on the engine |
| `--name` | this host's name | how it shows up in the worker list |
| `--labels` | none | comma-separated; what a node's `device` matches |
| `--python` | this interpreter | the interpreter node code runs on |
| `--parallel` | `1` | how many node calls it will take at once |
| `--artifact-url` | derived from `--url` | where the artifact store is, if not beside the socket |
| `--cpus` | what the job or the machine has | cores to advertise |
| `--gpus` | what the job says, else none | GPUs to advertise; never probed |
| `--ram-mb` | what the job or the machine has | memory to advertise, in MB |
| `--max-idle` | never | stop after this many seconds with nothing running |

`--python` is the important one. It is how this machine keeps its own wheels
(the CUDA build, the vendor SDK, the thing that will not install anywhere else)
without the engine ever installing them or knowing about them.

### What it says it has

A worker reports its inventory when it attaches (cores, GPUs and memory) and
the engine schedules against it: a node asking for two cores and a GPU goes to
a machine that has them free, not merely to one carrying the right label.

Cores and memory are read off the machine, or off the batch job that started
this worker (`SLURM_CPUS_ON_NODE`, `SLURM_MEM_PER_NODE`). **GPUs are never
probed.** Asking a vendor tool would make the one dependency two, so a GPU is
something the job says it was given (`SLURM_GPUS_ON_NODE`, `SLURM_JOB_GPUS`,
or `FLUKSIO_WORKER_GPUS`) or something you say with `--gpus`. A worker that
reports nothing still attaches and is scheduled by its label alone, as every
worker was before any of them reported anything.

The engine tells each call what it may use: thread caps, and the devices it
may see. The worker starts a process per call, so it applies them at the only
moment a numerical library still reads them: before the import.

`--max-idle` is for a worker something else started for one job, a batch
scheduler say. It exits when nothing has run for that long, so the allocation
goes back rather than idling until its walltime.

## Mint the token

On the engine, as a superuser:

```sh
curl -X POST $FLUKSIO/workers/tokens -H "Authorization: Bearer $TOKEN" \
  -H 'Content-Type: application/json' -d '{"name": "gpu-dev"}'
```

Shown once, valid for a year, since a worker is a machine somebody sets up and
leaves running. It is signed with the same keypair agent tokens use, so
rotating that key revokes every worker along with them.

??? note "A host where pip is not an option"

    The two files work copied into one directory and run with `python agent.py
    …`. The engine serves the runner itself at `GET /api/v1/workers/runtime`.
    It is the same module its own local workers run, deliberately standard
    library only.

## Send a node to it

A node declares the label of the machine it needs:

```json
{
  "id": "train",
  "device": "gpu",
  "device_policy": "require",
  "timeout": 7200
}
```

| `device_policy` | Behaviour when nothing carrying the label is attached |
|---|---|
| `require` (default) | the run stays `queued` and says what it is waiting for |
| `prefer` | it runs on the engine instead |

`prefer` is what makes a flow work before the GPU box exists. `require` is what
you want once it does.

!!! note "Not in the panel yet"

    `device` and `device_policy`, which machine a node's code runs on, are set
    through the API rather than the panel, with `PUT /flows/{name}`.

## What follows from this

- **The node's source travels with every call.** Nothing has to be deployed to
  the worker, and changing a node's code takes effect on the next execution.
- **A node bound to a device is compiled on that machine.** A node importing
  `torch` is correct on the GPU box and a missing module on the engine, so
  checking it here would fail something that is fine.
- **`import fluksio` inside a node is the worker's own reporter.** `emit`,
  `save_artifact`, `load_artifact`, installed before your code runs, so an
  installed `fluksio` package on that box never shadows it.
- **Cancelling a run kills what it is executing**, there or here, and leaves
  other runs of the same node alone.
- **If the worker disappears mid-call**, the run fails in seconds with
  `worker went away mid-call` rather than waiting out its timeout.
- **A worker sends a heartbeat every ten seconds while it executes**, so a long
  node is distinguishable from a dead socket. Ninety seconds of nothing at all,
  not even a heartbeat, fails the call as gone. A heartbeat says the *agent* is
  alive and nothing about the node, so it never satisfies a node's own timeout:
  one set to thirty seconds fires after thirty seconds of the node reporting
  nothing, wherever it runs.

## Artifacts across machines

An artifact reference names content by its hash, not a location, so it stays
valid wherever the store is reachable from. A worker that shares the engine's
filesystem writes to it directly; one that does not fetches and uploads over
HTTP, using the artifact endpoint beside the socket it already has. Either way
your node code is the same two calls.

A fetch is cached on the worker by digest, since content addressing means an
entry is never stale. Nothing expires on its own, so the cache is bounded by
size and the oldest fall out: `FLUKSIO_ARTIFACT_CACHE` says where it lives
(default a directory in the temporary directory) and
`FLUKSIO_ARTIFACT_CACHE_BYTES` how much it holds (default 1 GiB). Worth raising
where a worker reads the same large inputs repeatedly, and worth leaving alone
where it reads a media stream — those are chunks nothing asks for twice.

## Seeing what is attached

```sh
curl -s $FLUKSIO/workers -H "Authorization: Bearer $TOKEN" | jq
```

Name, labels, how many calls it will take at once, how many are in flight, when
it attached, when it was last seen, its Python version, a digest of its
environment, and what it says it has: cores, GPUs and memory.

## Upgrading

The engine and the worker speak a version-matched protocol, and a worker
announcing anything else is refused rather than half-understood. Protocol 2,
the one that carries inventory, is `fluksio-worker` 0.2.0. An older agent is
told so on the socket and stops, rather than retrying against an engine that
will never accept it; `pip install -U fluksio-worker` on that host is the whole
upgrade. Nothing changed in the runner served at `GET /api/v1/workers/runtime`,
so a host that copies its two files copies the same one as before.

## Machines from a batch scheduler

A cluster is not a machine that attaches and stays; it is a queue somebody else
owns. So Fluksio does not submit *nodes* to Slurm. It submits a job whose payload
is an ordinary worker dialling back in, and from there everything works the way
it already does: the same protocol, the same artifacts, the same cancellation.

Write the clusters into `provisioners.json` beside the flows:

```json
[{
  "type": "slurm",
  "name": "hpc",
  "login": "me@login.cluster",
  "ssh_key": "/secrets/hpc_ed25519",
  "engine_url": "wss://api.fluksio.com/api/v1/workers/attach",
  "max_idle_s": 300,
  "provision_timeout_s": 900,
  "profiles": [{
    "name": "gpu-small",
    "cpus": 8, "gpus": 1, "ram_mb": 65536,
    "labels": ["gpu"],
    "sbatch": ["--partition=gpu", "--gres=gpu:1", "--time=04:00:00"],
    "prerun": ["module load cuda/12", "source ~/venvs/flux/bin/activate"]
  }]
}]
```

A **profile** is what the scheduler is asked for, where a flavor is what a node
asks for. They are separate on purpose, and agree when you set them up to.

When a node needs a machine nothing attached can give, and a profile fits, the
engine `sbatch`es one over the system `ssh`, and the run waits meanwhile, saying
so. `prerun` owns the environment: a `module load`, or a venv with
`fluksio-worker` already in it. The generated script runs no `pip install`.

One outstanding request per profile, however often it is asked for. A job that
never attaches within `provision_timeout_s` is `scancel`led, as is anything
outstanding when the engine stops. `--max-idle` is what ends the job at the
other end, so an allocation goes back rather than idling to its walltime.

Both take `0` for "no limit": `provision_timeout_s: 0` waits for as long as the
queue does, which is what a cluster that queues overnight needs, and
`max_idle_s: 0` keeps the machine for the job's whole walltime.

`GET /workers/resources` reports what is outstanding and what last went wrong.

!!! note "It needs a route out"

    A compute node must be able to open a connection to the engine. That is
    true of most clusters and false of air-gapped ones; there is no staging
    path today.

## What a worker is not

It is not a second engine. Subscriptions, schedules, webhooks, the dashboards
and the run queue all stay in one process, which keeps a message having one
definition and a cron tick happening once. A worker executes node bodies.

Running two engines against one data directory is not supported. Distribute
work with workers.

## See also

- [Runs: pipelines that finish](../concepts/runs.md#running-a-node-somewhere-else)
- [Writing node code](nodes.md)
- [Getting started: data science](../getting-started/data-science.md)