app/docs/getting-started/data-science.md

# Getting started: data science

You have a training script. It works. What you do not have is any reliable
answer to "what was the learning rate on the run that got 94%?", and the
`results_final_v3_ACTUAL.csv` in your home directory is not helping.

This page adds Fluksio to what you already have. It takes about five minutes,
installs one Python package, and does not ask you to restructure anything.
## Install

```sh
cd my-research
pip install fluksio
fluksio serve
```

That is the whole installation. No Docker, no database server, no ports to
open, and no login. The first run prints something like:

```text
Created the admin account admin@example.com
  password: k3Qm-8vTpLdX
  Shown once. Change it from the dashboard.
Fluksio 0.1.0 — data in /home/you/my-research/.fluksio
  API      http://127.0.0.1:8000/api/v1
  Nodes    /home/you/my-research/.venv/bin/python
           your environment, adopted. Add packages with pip.
  No portal. Pair this installation with:
    fluksio enroll <code>
  Signed in as admin@example.com
           token in /home/you/my-research/.fluksio/client.json
```

Read the last two lines: you are already signed in. Signing in to your own
machine is a formality — the password was printed by the same process that
would have checked it — so `serve` writes the token itself and every command
below just works. `fluksio login` is for an engine somewhere *else*.

**Write that password down** anyway. It is shown once, and it is what the
dashboard asks for.

### One installation per project

`.fluksio` sits beside your code, and is found the way `.git` is — from the
directory you are standing in, or any directory above it. So two repositories
on one machine are two engines: separate flows, separate run history,
separate token, and no chance of one experiment's graph turning up in the
other's.

```text
~/research/protein-fold/
  .venv/          torch, fluksio
  .fluksio/       its own database, flows, artifacts, token
  myresearch/

~/research/climate-sim/
  .venv/          jax, fluksio
  .fluksio/       its own everything
  climate/
```

It holds a database and a credential, so it ignores itself from within — a
`.gitignore` of `*`, the same thing `uv` writes into `.venv`. Nothing to add
to your project's own.

Give them different ports (`--port`) if you want two running at once.

!!! tip "One engine for the machine instead"

    `fluksio serve --global` uses `~/.fluksio` — shared by every directory,
    which is what you want for a personal server rather than a project. When
    both exist, the banner says which one you are looking at and how to reach
    the other. `--data-dir` still names any directory outright, which is worth
    doing on a cluster where `$HOME` is a network filesystem: SQLite's
    write-ahead log does not work on NFS, and `fluksio serve` warns you when
    it notices.

!!! tip "Keep it running"

    The engine is meant to be resident. That is the whole reason submitting a
    run costs about 15 ms instead of the second a project-bootstrapping
    orchestrator spends before it does anything. Leave it in a `tmux` window,
    or write a small `systemd --user` unit for it.

## Talking to it over HTTP

Everything the commands do is the HTTP API, and some of this page shows it
directly. For that, take the token `serve` already wrote:

```sh
export FLUKSIO=http://127.0.0.1:8000/api/v1
export TOKEN=$(jq -r .token .fluksio/client.json)
```

While you are experimenting, the interactive schema at
<http://127.0.0.1:8000/docs> is the fastest way to see what is available.

For an engine on another machine, `fluksio login --url https://…` asks for a
password and stores the token the same way — in this project's `.fluksio`, or
with `--global` in `~/.fluksio`.

## Your packages are already there

If you installed Fluksio into the environment you work in — the venv that
already has torch or numpy in it — that is the environment your nodes run on.
Nothing to declare, nothing to install twice:

```sh
python -m venv .venv && . .venv/bin/activate
pip install torch numpy pandas      # what you were going to install anyway
pip install fluksio                 # and then this
fluksio serve
```

`fluksio serve` says which interpreter it settled on:

```text
  Nodes    /home/you/research/.venv/bin/python
           your environment, adopted. Add packages with pip.
```

That venv is yours. Add a package the way you added the rest — `pip install
scikit-learn` — and `fluksio sync` (or a restart) retires the workers so they
pick it up. The Modules screen lists what is installed and stays read-only,
because the alternative would be Fluksio deciding what belongs in an
environment it did not make.

!!! note "Your pins and ours share a site-packages"

    The cost of not having two environments: a package the engine depends on
    is one you can now upgrade out from under it. In practice this is what
    everybody does with every other tool in the venv, and the answer when it
    bites is the same — pin it back, or keep Fluksio somewhere separate with
    the venv of its own below.

### A venv of Fluksio's own

Sometimes you want the isolation instead: a shared installation, a container,
or an environment too precious to let a node's dependency near. Set
`NODE_VENV=managed` and Fluksio builds and owns one under the data directory:

```text
  Nodes    /home/you/.fluksio/user-venv/bin/python
           a venv of its own; the Modules screen installs into it.
```

Then the Modules screen is how packages get in — a pip manifest, installed
with `uv pip sync` and versioned alongside your flows, so what a run imported
is recorded with what it ran:

```sh
curl -X POST $FLUKSIO/modules/apply -H "Authorization: Bearer $TOKEN" \
  -H 'Content-Type: application/json' \
  -d "{\"requirements\": $(jq -Rs . < requirements.txt)}"
```

Adding a package takes effect immediately; nothing restarts. The Docker image
sets `NODE_VENV=managed` for itself, because the venv in it holds the app and
nothing of yours — so a container is always this case.

!!! tip "A GPU box works the same way"

    `pip install fluksio-worker` into the environment the training code runs
    in, and node code runs on it: `--python` defaults to the interpreter the
    agent was started with.

    ```sh
    fluksio worker --url ws://127.0.0.1:8000/api/v1/workers/attach \
      --token "$WORKER_TOKEN" --labels gpu
    ```

    Then give the node `device="gpu"` — see [Remote workers](../code/workers.md).

## Say which functions are nodes

A **flow** is a graph of nodes. A **batch flow** is one that runs on demand
from parameters to a result, which is what an experiment is. A node is one of
your own functions — it stays in your repository, imported by its siblings as
it always was.

Say your project looks roughly like this:

```python
# myresearch/train.py
def fit(dataset, lr, epochs=25):
    model = build_model()
    for epoch in range(epochs):
        loss = step(model, lr)
        print(f"epoch {epoch}: {loss}")     # ← this is what you are losing
    torch.save(model.state_dict(), "weights.pt")
    return loss
```

Two changes turn it into a node, and neither of them moves it:

```python
# myresearch/train.py
import fluksio
from fluksio import Port, node


@node(
    requires=["dataset", Port("lr", "float")],
    provides=[
        Port("loss", "float", stream=True),
        Port("weights", "artifact"),
        Port("final_loss", "float"),
    ],
    timeout=600,
)
def fit(dataset, lr, epochs=25):
    """Fit the model. A generator, so numbers escape while it is running."""
    model = build_model(fluksio.load_artifact(dataset))
    for epoch in range(epochs):
        loss = step(model, lr)
        yield {"loss": loss}                # ← published now, on the loss port
    torch.save(model.state_dict(), "weights.pt")
    return {
        "weights": fluksio.save_artifact("weights.pt"),
        "final_loss": loss,
    }
```

`yield` replaces `print`. Each one publishes on the node's `loss` port the
instant it happens, and the run keeps every value as a series — which is why
Fluksio has no `log_metric()` call. A metric that leaves through a port is a
message like any other: a chart can bind to it, a downstream node can consume
it, and it shows up on the canvas. A metric that escapes through a logging
function is invisible to all three.

`fluksio.save_artifact` handles the things too big to be messages — a
checkpoint, a dataset, a plot. It stores the bytes by their hash and returns a
small reference. Nothing changes about how you write the file.

The decorator returns the function untouched, so `fit(dataset, 0.05)` in a
test, a notebook or a debugger is exactly what it was before. Outside a run
`fluksio.emit` does nothing rather than failing, so a progress report never
stops your own code from running.

### What is declared, and what is not

- **Ports are declared, never inferred.** `requires` names the messages the
  node reads; each arrives as the parameter of the same name. A bare string is
  the shorthand — `"dataset"` takes its type from whoever provides it, so a
  message's type is written down exactly once.
- **Parameters with a default that are not ports become settings.** `epochs`
  above is stored with the flow and tunable on the canvas without touching
  this file.
- **The declaration is checked against the function.** A port with no matching
  parameter, or a parameter that is neither port nor setting, is an error when
  the module is imported — not when the node is first called.

!!! note "Where a `yield` cannot reach"

    If the number comes from inside somebody else's callback — Keras, Lightning,
    HuggingFace `Trainer` — `fluksio.emit(loss=...)` writes the same port the
    same way:

    ```python
    model.fit(callbacks=[LambdaCallback(
        on_epoch_end=lambda epoch, logs: fluksio.emit(loss=logs["loss"])
    )])
    ```

### Declaring what a node needs

A training node is not like the rest of a flow. NumPy, JAX and PyTorch each
size their thread pool to every core they can see, so a few of them at once
oversubscribe the machine badly enough to starve the engine itself — the API
stops answering and every client waiting on a run dies with it. On a GPU the
same shape deadlocks: two processes each preallocating most of the card sit at
zero utilisation with nothing failing and nothing to read.

Say what one execution takes, and the engine holds it:

```python
@node(..., resources={"cpus": 2, "env": {"XLA_FLAGS": "--xla_cpu_multi_thread_eigen=false"}})
def fit(dataset, lr, epochs=25):
    ...

@node(..., resources={"gpus": 1, "env": {"XLA_PYTHON_CLIENT_MEM_FRACTION": "0.9"}})
def finetune(checkpoint):
    ...
```

Two things follow. The node waits its turn rather than starting alongside more
of itself than the machine has room for — the same waiting it already does for
a worker. And the worker it runs in is *started* with thread limits matching
what it was given, because that is the only moment a numerical library reads
them; a GPU node is told which card is its through `CUDA_VISIBLE_DEVICES`, and
nothing else is given that card while it runs.

`env` is for the tuning the engine must not invent. `XLA_FLAGS` is one composed
string whose contents depend on the version you have installed, so writing it
for you would silently replace whatever you had put there.

Declaring nothing is the default and is right for most nodes — a poll, a
threshold, a message on its way somewhere. Those share the engine's worker pool
and are given a fair share of `FLOW_CPUS` as a thread cap, which is what stops
several of them at once from each sizing to the whole box.

### Ask for a size by name

Cores and gigabytes are a property of the machines you have, and those change.
A node that names a **flavor** keeps meaning something afterwards:

```python
@node(..., resources="gpu-small")
def finetune(checkpoint):
    ...

@node(..., resources={"flavor": "medium", "duration_s": "2h"})
def fit(dataset, lr, epochs=25):
    ...
```

The flavor is read every time the node is built, so editing it changes what the
next run gets. `fluksio flavors` lists them; the Workers screen edits them.

`ram` takes `"2G"` and `duration_s` takes `"30m"`, and a flavor already says how
much — pass one or the numbers, not both. `duration_s` is a statement about the
node for whoever is planning around it, not a limit; the limit is `timeout`.

### Where it runs

This is one decision, not two. Of every machine attached — this engine and each
worker — the node goes to one that can grant what it asked for and has it free.
So a node asking for a card finds the box that has one, without naming it:

```python
@node(..., resources={"gpus": 1})          # wherever there is a card
@node(..., device="gpu", resources="gpu-small")   # that box, and this much of it
```

If nothing attached can ever grant the ask, it is cut down to what is here and
runs anyway — a flow written on a cluster still has to work on a laptop. If
something *could* but is busy, the node waits and says so.

Ask what is free, and what is waiting for it, at
`GET /api/v1/workers/resources`, on the Workers screen, or in `fluksio status`.

!!! note "Accounted, not enforced"

    Nothing stops a node that ignores its declaration; the numbers are
    bookkeeping plus the environment its libraries read. Real enforcement —
    cgroups, rlimits — is a next step, not this one.

## Say which nodes make a flow

Membership is a list, not a directory layout: the functions can live wherever
they already do.

```python
# myresearch/pipeline.py
from fluksio import Flow, Port, use

from myresearch.data import augment, prepare
from myresearch.evaluate import evaluate
from myresearch.train import fit

train = Flow(
    "train",
    nodes=[prepare, fit, evaluate],
    inputs=[Port("lr", "float", initial=0.01)],
    outputs=["score", "final_loss"],
)
```

`inputs` are the run's parameters, with the value a run gets when it names
none; `outputs` are what a run reports as its result.

Nodes are connected because one **provides** a message another **requires** —
never because one imported the other. Importing `fit` into a second flow means
"the same code", not "wired to it".

Which is how the same function serves two flows, rewired and reconfigured for
each:

```python
finetune = Flow(
    "finetune",
    nodes=[
        prepare,
        augment,
        use(fit, wire={"dataset": "augmented"}, epochs=3),
        evaluate,
    ],
    inputs=[Port("lr", "float", initial=1e-4)],
    outputs=["score"],
)
```

`use(fn, ...)` is one use of a node in one flow: `wire` binds a port to a
different message, keyword arguments override settings, and `id=` tells two
uses of one function apart. `train` above is unaffected by any of it.

## Sync it

```sh
fluksio sync myresearch
```

That imports the package, checks every declaration, and uploads each flow with
a generated body per node:

```python
# generated by fluksio sync from myresearch/train.py — edit that file instead
import sys

_REPO = '/home/you/my-research'
if _REPO not in sys.path:
    sys.path.insert(0, _REPO)

from myresearch.train import fit


def process(dataset, lr, **settings):
    return (yield from fit(dataset=dataset, lr=lr, **settings))
```

So the flow store still holds a complete, runnable, git-versioned definition —
the body simply happens to import rather than duplicate. Your code stays in
your repository, under your version control.

The upload is stamped with that repository's commit, and every run records it
alongside the store's own. `--dry-run` prints all of this and uploads nothing;
`--no-publish` leaves it as a draft.

!!! warning "Sync after you edit"

    The engine's workers are long-lived and hold your imported modules in
    memory, so an edit to `myresearch` is invisible until they are retired —
    which is what every `fluksio sync` does, including one that has nothing to
    upload. If you are editing many times an hour, attach your own interpreter
    as a worker instead: it starts a process per call and therefore reads your
    code fresh every run.

    ```sh
    fluksio worker --url ws://127.0.0.1:8000/api/v1/workers/attach \
      --token "$WORKER_TOKEN" --labels local --python "$(which python)"
    ```

    Then give the node `device="local"`.

!!! note "The repository path is a deployment detail"

    The generated body puts your repository on `sys.path` by absolute path,
    which is right for the machine you synced from and meaningless in a
    container. For anything else, install the package where the node runs —
    `-e /home/you/my-research` in the module manifest, or
    `myresearch @ git+ssh://…@a1b2c3d`, which travels where a path does not.

### Which hardware a node runs on

`device` picks the worker, the same way an executor does elsewhere:

```python
@node(requires=["dataset"], provides=[...], device="gpu", device_policy="prefer")
def fit(dataset, lr, epochs=25):
    ...
```

`device_policy="require"` (the default) waits for a worker carrying that label;
`"prefer"` runs it locally when none is attached, which is what you want while
the GPU box is not switched on. See [Remote workers](../code/workers.md).

### What the canvas does with a synced flow

Its node bodies are generated, so the editor shows them read-only and says
where the real code is. Everything else behaves as usual — but ports and
settings changed there are overwritten by the next sync, which is the point of
the repository being the source of truth. A node whose code you edit on the
canvas makes the next sync stop and say so rather than discarding your edit;
`--force` overrides that.

## Run it

```sh
fluksio run train --lr 0.003 --seed 7
fluksio runs --flow train
```

`run` syncs first, so after an edit the command is just `fluksio run` — there
is no step to forget. `--no-sync` skips it for a tight loop where nothing
changed, since syncing retires the workers and the next call pays its imports
again.

`--lr` is typed by the flow's own inputs, so `0.003` arrives as a float. A
parameter you did not declare, or one of the wrong type, is refused before
anything executes. It answers immediately with a queued run — training is
measured in hours, so nothing waits for it unless you pass `--wait`, or
`--follow`, which waits and prints the numbers as the run reports them.

No engine has to be up for any of this: `fluksio run --local` boots the
engine inside the command instead, on the same `.fluksio` — the same database,
artifacts and history a served one would use. It costs a few seconds of
startup per invocation against the ~15 ms of submitting to an engine that is
already up, so it is for the run you want now rather than the loop you are
iterating in.

A second run of a flow you did not change mostly does not execute. Each of
your nodes is fingerprinted by its settings, the ports it declares, the values
it reads, and a digest of your repository's Python files — so editing anything
your node calls into invalidates it, not only the decorated function itself.
One an earlier run already answered is restored from that run rather than run
again — reported as `cached`, so a flow with nothing left to do finishes as
`(3/3 cached)`. Change `--lr` and only the nodes downstream of it run. The
digest is blunt on purpose: an edit anywhere in the repository re-runs
everything. `--no-cache` turns caching off for one run, `@node(...,
cache=False)` for one node; the caveat and the details are in
[Stage caching](../concepts/runs.md#stage-caching).

From Python, the flow you declared is also the handle to its runs:

```python
from myresearch.pipeline import train

run = train.submit(lr=0.003, seed=7).wait()
print(run.status, run.result)
print(run.metrics("train.loss")[-1])       # the whole series is kept
open("weights.pt", "wb").write(run.download("train.weights"))
```

Or over HTTP, which is what both of those are:

```sh
curl -X POST $FLUKSIO/runs/flows/train -H "Authorization: Bearer $TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"params": {"lr": 0.003}, "seed": 7}'

curl -s $FLUKSIO/runs/<id> -H "Authorization: Bearer $TOKEN" | jq
curl -s "$FLUKSIO/runs/<id>/metrics?name=train.loss" -H "Authorization: Bearer $TOKEN" | jq
```

The run carries its parameters, a digest of them, the seed, its result, how
long each node took, what it logged, every artifact it produced, and both
commits — the flow store's and your repository's. That is the answer to "what
was the learning rate on the run that got 94%?". When a run failed,
`run.failures` is the node that did it, with its traceback and its logs.

Beside your commit is `code_digest`, and `fluksio runs` prints the pair as
`a1b2c3d-dirty+9f0e1a2`. The commit alone cannot identify what ran: your node
bodies are imports, so the engine executes whatever is on disk when the worker
starts, and an uncommitted tree stamps `-dirty` for every run it ever produces.
The digest is over the modules this flow's nodes actually reach — not the
whole repository, so it moves when the code behind a number moves and stays
put when a notebook beside it changes — and it is read at the moment the run
starts, so in a sweep whose runs queue for hours each one records the code
that actually executed it rather than the code that was there when you
submitted. It is what makes the column worth joining an
[exported table](../concepts/runs.md#taking-it-into-a-dataframe) on.

### When the engine is busy

A driver script outlives the engine being slow, because a sweep is hours long
and a blip is seconds:

- **Reads are repeated.** Every GET, and the writes that mean the same thing
  twice, are tried up to three more times on a transport error or a `502`,
  `503` or `504`, waiting 1 s, 2 s, then 4 s. `Client(retries=0)` turns that
  off.
- **Submitting is safe to repeat.** Each `submit()` mints a key the engine
  stores with the run, so an attempt that timed out on the way back is
  answered with the run it already made rather than starting a second one. A
  sweep keys every entry, so retrying a half-created one creates only what is
  missing.
- **Waiting tolerates a gap.** `wait()` and `--follow` survive five failed
  polls in a row before giving up. An answer is not a gap: a `404` for a run
  that is gone stops immediately.
- **Timeouts are split** — 5 s to connect, 120 s to read, so a wrong URL fails
  at once while a busy engine is given longer than the slowest thing it does
  on purpose. `Client(timeout=…)` takes a number or an `httpx.Timeout`.

A command that still cannot reach the engine says
`fluksio: engine not answering (…)` and names the run it started, which is
running on the engine regardless — `fluksio runs` finds it again.

## Sweep it

A grid search and an ensemble are the same submission, run in parallel:

```sh
fluksio sweep train --param lr=0.001,0.003,0.01 --wait
```

Every combination of the lists you give, so a second `--param` is a grid
rather than a second sweep. Where the set you want is not a product, build the
list yourself and post it:

```sh
curl -X POST $FLUKSIO/runs/flows/train/sweep -H "Authorization: Bearer $TOKEN" \
  -H 'Content-Type: application/json' -d '{"runs": [
    {"params": {"lr": 0.001}, "seed": 1},
    {"params": {"lr": 0.003}, "seed": 1},
    {"params": {"lr": 0.010}, "seed": 1}
  ]}'
```

They share a `group_id`, so the sweep is `GET /runs?group=…`. Running them
concurrently is safe because each run gets a state backend of its own — two
runs of one flow cannot overwrite each other's values.

Compare the curves in one call:

```sh
curl -s "$FLUKSIO/runs/series/compare?ids=$A,$B,$C&metric=train.loss" \
  -H "Authorization: Bearer $TOKEN"
```

which answers in exactly the shape a chart widget draws.

## Take it into a dataframe

The analysis itself wants a table, and `export` writes the two an analysis
asks for — the curves long, and one row per run with the parameters that
varied beside its final numbers:

```sh
fluksio export metrics --flow train --name train.loss -o curves.csv
fluksio export runs    --flow train --status ok      -o arms.csv
```

Both carry the run id on every row, and `arms.csv` carries the commit and the
code digest beside it, so an exported file still says what produced its
numbers. Numbers inside a record are columns of their own —
`metric.final_metrics.train_loss` — and `--metrics` and `--params` take those
dotted paths to narrow the table. Metric names are flow-qualified, so
`--list` prints the ones a selection carries. `Client.export_metrics()` and
`Client.export_runs()` answer the same rows to a notebook, ready for
`pandas.DataFrame`.

## Looking at them in the portal

None of the above needs a browser, but there is one. **Runs** is the table of
everything that has run: filter it to a flow, to a status, or to one sweep —
where it grows a column per parameter that actually varied — and tick two runs
to put their curves side by side.

A run records its metrics under the same names a dashboard binds to, so a
dashboard is another way of reading one. **Open in dashboard** carries the
runs you picked along in the URL, and every widget then answers for those runs
instead of for the live engine. If the flow has no dashboard yet, the same
menu builds one from the ports the flow declares.

See [Runs → Looking at what ran](../concepts/runs.md#looking-at-what-ran).

## What sync does, at the API level

Nothing here is privileged: `sync` is a client, and a flow is a document you
can PUT yourself. The whole of it, for the one-node case:

```python
"""Create the `train` flow by hand. What `fluksio sync` automates."""

import httpx

API = "http://127.0.0.1:8000/api/v1"
api = httpx.Client(base_url=API, timeout=60)
token = api.post(
    "/login/access-token",
    data={"username": "admin@example.com", "password": "k3Qm-8vTpLdX"},
).json()["access_token"]
api.headers["Authorization"] = f"Bearer {token}"

api.put("/flows/train", json={
    "name": "train",
    "title": "Model training",
    # Batch: nothing is activated, nothing fires until a run asks.
    "mode": "batch",
    "inputs": [
        {"spec": {"name": "lr", "dtype": "float"}, "initial": 0.01},
        {"spec": {"name": "epochs", "dtype": "int"}, "initial": 50},
    ],
    "outputs": ["final_loss", "weights"],
    "nodes": [{
        "id": "train",
        "type": "python",
        "title": "Fit the model",
        # An *idle* timeout once the node streams: this is how long it may go
        # quiet, not how long it may run.
        "timeout": 600,
        "requires": [
            {"name": "lr", "dtype": "float"},
            {"name": "epochs", "dtype": "int"},
        ],
        "provides": [
            # `stream` says this port publishes repeatedly during one execution.
            {"name": "loss", "dtype": "float", "stream": True},
            {"name": "final_loss", "dtype": "float"},
            {"name": "weights", "dtype": "artifact"},
        ],
    }],
}).raise_for_status()

api.put("/flows/train/nodes/train/source",
        json={"code": open("train_node.py").read()}).raise_for_status()

version = api.get("/flows/train").json()["definition"]["version"]
api.post("/flows/train/publish", json={"version": version}).raise_for_status()
```

Two things worth noticing, because the decorators only move where they are
said. Ports are declared, not inferred — `process(lr, epochs)` gets its
arguments from the ports of the same name, and the types are checked on every
value. And saving writes a *draft*; `publish` is what the engine picks up.
That separation is what lets you edit a flow that is running.

A flow uploaded this way carries no `origin`, which is what tells the canvas —
and the next `fluksio sync` — that it was not generated. See
[The API](../code/api.md).

## Small scripts you are just playing with

The same machinery, minus the ceremony. If what you want is "keep a record of
every version of this thing I keep tweaking", note that **your flows are
already a git repository**:

```sh
cd .fluksio/flows
git log --oneline
```

Every save is a commit — the node source, the parameters, the graph. A run
records the commit it ran at, so `git show` on that hash is literally the code
that produced the number. You get the versioning without adopting anything.

For quick iteration, keep the flow small (one node is fine), keep the engine
running, and submit from wherever you are working:

```python
from myresearch.pipeline import train

for lr in (0.001, 0.003, 0.01):
    train.submit(lr=lr)
```

A submit is around 15 ms, so calling that in a loop is a reasonable thing to do.

## Get a browser onto it

The pip install gives you the engine and the API, not a web interface — a
machine with no inbound route cannot serve one usefully anyway. To see the
canvas, the run history and live loss curves, pair the installation with a
portal, which serves the dashboard from its side:

```sh
fluksio enroll <claim-code> --portal https://hub.fluksio.com
```

Get the claim code from the portal under **Installations → Add installation**.
Nothing needs to be exposed: your machine dials out and holds the connection
open. See [Accounts and the portal](../interface/portal.md).

If you would rather stay entirely offline, run the dashboard SPA yourself from
the app's Docker image — see the [facility path](facility-automation.md), which
is the same stack.

## Where to go next

- [Runs: pipelines that finish](../concepts/runs.md) — the full picture:
  artifacts, sweeps, durability, what happens when your engine dies mid-training
- [Writing node code](../code/nodes.md) — generators, settings, what a node may
  and may not do
- [The command line](../code/cli.md) — `login`, `sync`, `run`, `runs`, `sweep`
  in full
- [Remote workers](../code/workers.md) — send the training node to the GPU box
  and keep the rest on your laptop
- [The flow editor](../interface/flow-editor.md) — once you have a portal, this
  is where the graph gets easier to change than the script did