stroblme
dea25ec08d
The backend image now builds the SPA and serves it on the API's own origin (fluksio/spa.py), so an instance is a single container with no proxy, DNS, port 80 or .env: `docker compose up -d` with the new compose.yml runs it on localhost:8080. It starts on fluksio serve (persisted secret key, first admin printed once) and brings its own Redis when REDIS_HOST is unset. Slimmer: python:3.13-slim, no dev group, uv once, fastapi without the cloud CLI. Optional features are per-feature extras (mqtt, influx, email, sentry, ml, server for all) picked with the EXTRAS build argument; numpy moved to `ml`. ffmpeg left the image for imageio-ffmpeg via the Modules screen. The operator stack runs the same image with app. and api. routed to it; the nginx frontend service, the LAN nginx proxy and DOMAIN in the backend are gone, and the MCP issuer is FRONTEND_HOST.
118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
"""A small perceptron node, kept as a worked example of numeric logic."""
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from __future__ import annotations
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import logging
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import threading
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from collections.abc import Iterable
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from typing import Any
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from pydantic import BaseModel, ConfigDict
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from fluksio.flow.messages import MessageSpec
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from fluksio.flow.nodes.base import Node
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logger = logging.getLogger(__name__)
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def _numpy() -> Any:
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"""numpy, which is the `ml` extra.
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Imported when a node is built rather than at boot, so an instance without
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it still offers every other type, and a node placed without it says so.
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"""
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try:
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import numpy
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except ImportError:
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raise RuntimeError(
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"the mlp node needs the ml extra: pip install 'fluksio[ml]', "
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"or build the image with EXTRAS=ml"
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) from None
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return numpy
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class MLPNode(Node):
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"""
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Multi-Layer Perceptron node for neural network processing in pipelines.
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This node implements a simple single-layer neural network that applies
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weights and biases to input values. Weights and biases are randomly
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initialized using the provided random number generator.
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The computation follows the standard neural network formula:
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output = weights @ inputs + biases
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:param requires: Input messages consumed by this node.
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:type requires: MessageSpec | list[MessageSpec]
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:param provides: Output messages produced by this node.
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:type provides: MessageSpec | list[MessageSpec]
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:param params: Parameters dict containing:
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- ``rng`` (numpy.random.Generator): Random number generator for weight initialization
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- Additional node parameters
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:type params: dict
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:param name: Name for this node.
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:type name: str
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:example:
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>>> import numpy as np
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>>> rng = np.random.default_rng(seed=42)
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>>> mlp = MLPNode(
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... requires=[MessageSpec(name="input1", dtype=DType.FLOAT), MessageSpec(name="input2", dtype=DType.FLOAT)],
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... provides=[MessageSpec(name="output", dtype=DType.FLOAT)],
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... params={"rng": rng},
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... name="mlp_layer1",
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... )
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"""
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class Params(BaseModel):
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"""Weights are drawn from ``seed``, so a node reloads identically."""
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model_config = ConfigDict(extra="allow")
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seed: int = 0
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def __init__(
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self,
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requires: MessageSpec | Iterable[MessageSpec] = (),
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provides: MessageSpec | Iterable[MessageSpec] = (),
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params: dict[str, Any] | None = None,
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name: str | None = None,
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):
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super().__init__(
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self._forward,
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requires=requires,
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provides=provides,
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params=params,
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name=name or "mlp",
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)
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cfg = self.Params.model_validate(self.params)
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np = self._np = _numpy()
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rng = np.random.default_rng(seed=cfg.seed)
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num_inputs = max(1, len(self.input_ports))
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num_outputs = max(1, len(self.output_ports))
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self.weights = rng.normal(loc=1, size=(num_outputs, num_inputs))
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self.biases = rng.normal(loc=0, size=(num_outputs,))
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def _forward(
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self, params: dict[str, Any], **kwargs: float
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) -> dict[str, Any] | None:
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"""Apply ``weights @ inputs + biases`` to the incoming values."""
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if not self.output_ports:
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return None
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logger.info(
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"Executing MLP node in thread %s: %s",
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threading.current_thread().name,
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self.id,
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)
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np = self._np
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if kwargs:
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input_array = np.array([float(v) for v in kwargs.values()])
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else:
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input_array = np.array([1.0]) # Bias only, for source nodes.
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outputs = np.dot(self.weights, input_array) + self.biases
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return {p.port: float(outputs[i]) for i, p in enumerate(self.output_ports)}
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