KPN/SPEC.md

KPN++ — Kahn Process Network Library Specification

Overview

A C++20 template-metaprogramming library for building Kahn Process Networks, where each node wraps a function/method, runs in its own thread, and communicates via bounded FIFO queues. Includes nanobind bindings for Python graph construction and prototyping.

---

Project Structure

kpn++/
├── CMakeLists.txt
├── include/kpn/
│   ├── fixed_string.hpp    # NTTP string type for named ports
│   ├── traits.hpp          # Function signature introspection
│   ├── channel.hpp         # Bounded FIFO channel + storage policy
│   ├── node.hpp            # Node wrapper + thread management
│   ├── port.hpp            # Input/Output port handles
│   ├── network.hpp         # Graph builder + orchestrator/watchdog
│   ├── variant_node.hpp    # Runtime-typed node for Python graphs
│   └── python/
│       └── bindings.hpp    # Nanobind binding helpers
├── src/
│   └── network.cpp         # Orchestrator thread impl
├── tests/
├── examples/
│   ├── 01_hello_pipeline/
│   ├── 02_named_ports/
│   ├── 03_multi_output/
│   ├── 04_storage_policy/
│   ├── 05_error_handling/
│   ├── 06_watchdog/
│   ├── 07_python_network/
│   ├── 08_python_subport/
│   └── 09_opencv_cellshade/    # optional, requires OpenCV
└── python/
    └── kpn_python.cpp      # Nanobind module definition

---

Component 0 — fixed_string.hpp: NTTP String

Named ports use C++20 non-type template parameters (NTTPs). std::string_view and const char* are not valid NTTPs because they are not structurally comparable. The standard solution is a fixed_string literal type with constexpr internal storage.

template<std::size_t N>
struct fixed_string {
    char data[N]{};
    constexpr fixed_string(const char (&s)[N]) { std::copy_n(s, N, data); }
    constexpr bool operator==(const fixed_string&) const = default;
    constexpr std::string_view view() const { return {data, N - 1}; }
};

// Deduction guide — required so fixed_string("img") works as an NTTP.
// Without it the compiler cannot infer N and the named-port API does not compile.
template<std::size_t N>
fixed_string(const char (&)[N]) -> fixed_string<N>;

fixed_string<3> and fixed_string<6> are distinct types, so input<"img">() and input<"sigma">() produce different template instantiations — this is intentional and enables zero-overhead compile-time port dispatch.

Named-port lookup uses a constexpr function over the name pack. It returns a sentinel npos on miss rather than static_assert-ing internally, so the assertion fires at the input<"img">() call site — giving the user a readable error at the point of use instead of deep in template instantiation:

inline constexpr std::size_t npos = std::size_t(-1);

template<fixed_string Name, fixed_string... Names>
constexpr std::size_t index_of() {
    std::size_t i = 0;
    bool found = false;
    ((Name == Names ? (found = true) : (found ? 0 : ++i)), ...);
    return found ? i : npos;
}

// Used at the call site:
template<fixed_string N>
auto input() {
    constexpr std::size_t idx = index_of<N, InputNames...>();
    static_assert(idx != npos, "unknown input port name");
    return input<idx>();
}

---

Component 1 — traits.hpp: Function Introspection

Extracts parameter types and return type from any callable at compile time.

// For: Image blur(Image in, float sigma)
// function_traits<decltype(blur)>::args     == std::tuple<Image, float>
// function_traits<decltype(blur)>::return_t == Image

// For multi-output: std::tuple<Image, Mask> detect(Image in)
// return_t == std::tuple<Image, Mask>  →  2 output ports
// return_t == Image                    →  1 output port (normalised to tuple<Image> internally)
// return_t == void                     →  0 output ports (sink node)

Handles: free functions, lambdas, std::function, member function pointers.

A helper alias normalises the return type to always be a tuple for uniform handling in run_loop:

template<typename T>
using normalised_return_t =
    std::conditional_t<is_tuple_v<T>, T, std::tuple<T>>;
// void return → std::tuple<> (empty tuple, zero output ports)

---

Component 2 — channel.hpp: Bounded FIFO + Storage Policy

Storage Policy

The type stored in a channel depends on a specialisable trait. Users can override it for any type:

template<typename T>
struct channel_storage_policy {
    static constexpr bool by_value =
        std::is_trivially_copyable_v<T> && sizeof(T) <= 8;
};

// User opt-in to value semantics for a small struct:
template<> struct channel_storage_policy<MySmallStruct> {
    static constexpr bool by_value = true;
};

// Derived storage type:
template<typename T>
using channel_storage_t = std::conditional_t<
    channel_storage_policy<T>::by_value,
    T,
    std::shared_ptr<const T>
>;

Channel

Channel<T> stores channel_storage_t<T> internally. The producer calls push(T value) and the channel transparently wraps it in make_shared<const T> when needed. All consumers of the same channel receive the same shared_ptr — no copies of large objects.

run_loop dereferences shared_ptr<const T> before passing to the wrapped function, so a function declared void f(const Image& img) works naturally and the compiler enforces immutability — no policy enforcement or const_cast needed.

template<typename T>
class Channel {
public:
    using storage_type = channel_storage_t<T>;

    explicit Channel(std::size_t capacity = 5);

    void push(T value);        // wraps in shared_ptr<const T> if needed; throws on overflow
    T    pop();                // blocks (KPN semantics); unwraps shared_ptr if needed
    bool try_pop(T& out, std::chrono::milliseconds timeout);

    std::size_t size()     const;
    std::size_t capacity() const;
};

class ChannelOverflowError : public std::runtime_error {};

Ownership

A Channel<T> is owned by its consumer node — it lives as a member of the destination node. The producer node holds a non-owning raw pointer to push into it. The channel is destroyed when its consumer is destroyed, which is the correct lifetime.

The Network itself is non-owning — nodes are declared by the user and outlive the network. net.add("name", node) registers a raw pointer; the user is responsible for keeping nodes alive for the network's lifetime. This avoids type-erasure ownership complexity and keeps node construction explicit.

Backpressure and Shutdown — accepting_ Flag

Each channel carries a single std::atomic<bool> accepting_ (default true). This is the sole shutdown mechanism — no try_pop polling, no sentinel values, no drain logic.

template<typename T>
class Channel {
    std::atomic<bool> accepting_{true};
public:
    void push(T value) {
        if (!accepting_.load(std::memory_order_relaxed)) return; // silently drop
        // normal push — throws ChannelOverflowError if full
    }

    void enable()  { accepting_.store(true,  std::memory_order_relaxed); }
    void disable() {
        accepting_.store(false, std::memory_order_relaxed);
        clear();        // drop all queued items immediately
        cv_.notify_all(); // unblock any waiting pop()
    }
};

Who flips the flag: the consumer node — it's the channel owner. node.stop() calls disable() on all its input channels. node.start() calls enable(). The producer never touches the flag; it calls push() and if the channel is disabled the value is silently dropped and the producer continues.

Overflow (push() on a full, accepting channel) still throws ChannelOverflowError — this signals a design error (undersized FIFO) and is unchanged.

Blocking pop() unblocks immediately when disable() is called (via cv_.notify_all()), and throws ChannelClosedError if the queue is empty and the channel is disabled.

try_pop Purpose

try_pop exists for watchdog polling only — not for shutdown (the accepting_ flag handles that) and not for normal processing. The watchdog uses it to probe whether a node is making progress without blocking the watchdog thread.

Note (C++ compile-time graphs): In a fully compiled C++ graph the variant never appears. The compiler wires Channel<Image> to Channel<Image> directly. The variant is a pure compile-time construct used only for type-checking and generates zero runtime overhead.

---

Component 3 — port.hpp: Port Handles

template<typename NodeT, std::size_t Idx>
struct InputPort  { NodeT& node; };

template<typename NodeT, std::size_t Idx>
struct OutputPort { NodeT& node; };

Nodes expose port handles via:

// By index — always available
node_a.input<0>()    // returns InputPort<NodeA, 0>
node_b.output<1>()   // returns OutputPort<NodeB, 1>

// By name — only valid when names were provided at make_node time
node_a.input<"img">()
node_b.output<"edges">()

Named access resolves to an index at compile time via index_of (see fixed_string.hpp). Zero runtime cost — the name dispatch is fully eliminated by the compiler.

---

Component 4 — node.hpp: Node Wrapper

template<
    auto Func,
    fixed_string... InputNames,   // optional; count must match arity or be 0
    fixed_string... OutputNames   // optional; count must match output count or be 0
>
class Node {
public:
    explicit Node(std::size_t fifo_capacity = 5);

    void start();
    void stop();   // signals thread to finish current item then exit

    // Port access — by index
    template<std::size_t I>  auto input();
    template<std::size_t I>  auto output();

    // Port access — by name (compile error if names were not provided)
    template<fixed_string N> auto input();
    template<fixed_string N> auto output();

    static constexpr std::size_t input_count;
    static constexpr std::size_t output_count;

private:
    void run_loop();
    // Pops each input channel, dereferences shared_ptr if needed,
    // calls Func, unpacks the normalised tuple return,
    // pushes each element to its output channel.

    std::thread thread_;
    // Input channels owned here (one per input port).
    // Output channel pointers (non-owning) set at connect time.
};

Factory syntax — in<> / out<> tag structs:

A flat name pack make_node<f, "a", "b", "c"> is ambiguous (where do inputs end?). Option chosen: in<...> and out<...> tag types that wrap the name packs unambiguously. Both are optional; omitting either means those ports are index-only.

// Tag types (trivial, no data):
template<fixed_string... Names> struct in  {};
template<fixed_string... Names> struct out {};

// Factory:
// No names
auto node = make_node<my_func>(/*fifo_capacity=*/10);

// Input names only
auto node = make_node<my_func, in<"img","sigma">>(10);

// Both input and output names
auto node = make_node<my_func, in<"img","sigma">, out<"blurred","mask">>(10);

Wrong name count is a compile error. The Node class static_asserts that sizeof...(InputNames) == 0 || sizeof...(InputNames) == input_count (and same for outputs). Without this, a mismatch between name count and arity produces an unreadable template error.

static_assert(
    sizeof...(InputNames) == 0 || sizeof...(InputNames) == input_count,
    "make_node: number of input names must match function arity, or provide none"
);

Multi-output functions must return std::tuple<...>. Single return accepted as-is. void return = sink node (no output ports).

---

Component 5 — network.hpp: Graph Builder + Orchestrator

Network is non-owning — nodes are declared by the user and must outlive the network. add() registers a raw pointer. Graph construction uses a builder pattern so the full topology is known before build(), enabling cycle detection and topological ordering.

class Network : public INode {   // Network is itself an INode — enables sub-networks
public:
    // Register a node by name. NodeT must satisfy INode. Network holds a raw pointer.
    template<typename NodeT>
    Network& add(std::string name, NodeT& node);

    // Connect output port of src to input port of dst.
    // Type mismatch → static_assert at compile time.
    template<typename SrcNode, std::size_t SrcIdx,
             typename DstNode, std::size_t DstIdx>
    Network& connect(const std::string& src_name, OutputPort<SrcNode, SrcIdx>,
                     const std::string& dst_name, InputPort<DstNode,  DstIdx>);

    // Expose an internal node's input/output as a boundary port of this (sub-)network.
    // Allows a Network to be connected into a larger Network like a single node.
    template<typename NodeT, std::size_t Idx>
    Network& expose_input(std::string boundary_name, InputPort<NodeT, Idx>);

    template<typename NodeT, std::size_t Idx>
    Network& expose_output(std::string boundary_name, OutputPort<NodeT, Idx>);

    // DFS cycle check + topological sort. Throws NetworkCycleError on cycles.
    Network& build();

    void start() override;   // starts all internal nodes in topological order
    void stop()  override;   // stops all internal nodes in reverse order; disables channels
    bool running() const override;

    void set_watchdog_interval(std::chrono::milliseconds);

    using ErrorHandler = std::function<void(std::string_view node_name, std::exception_ptr)>;
    void set_error_handler(ErrorHandler);

private:
    std::map<std::string, INode*>                   nodes_;      // non-owning
    std::map<std::string, std::vector<std::string>> adj_;
    std::vector<std::string>                        topo_;
    std::jthread                                    watchdog_;
    std::chrono::milliseconds                       watchdog_interval_{500};
    ErrorHandler                                    error_handler_;
};

Node lifetime contract: nodes must outlive the Network. The typical pattern is to declare nodes and the network in the same scope:

// Nodes declared first — they own their input channels
auto blur   = make_node<blur_func,   in<"img","sigma">>(10);
auto detect = make_node<detect_func, in<"img">>(10);

Network net;
net.add("blur",   blur)
   .add("detect", detect)
   .connect("blur",   blur.output<0>(),          "detect", detect.input<0>())
   .connect("blur",   blur.output<"blurred">(),  "detect", detect.input<"img">())
   .build();
net.start();

Sub-networks — because Network implements INode, it can be registered inside a larger Network as a named node. Boundary ports declared via expose_input / expose_output make the internal nodes' ports available to the outer graph:

// Inner sub-network
auto stage1 = make_node<preprocess>(5);
auto stage2 = make_node<enhance>(5);
Network pipe;
pipe.add("pre", stage1).add("enh", stage2)
    .connect("pre", stage1.output<0>(), "enh", stage2.input<0>())
    .expose_input("img",    stage1.input<0>())
    .expose_output("result", stage2.output<0>())
    .build();

// Outer network treats `pipe` as a single node
auto sink = make_node<display>(5);
Network top;
top.add("pipe", pipe).add("sink", sink)
   .connect("pipe", pipe.output<"result">(), "sink", sink.input<0>())
   .build();
top.start();

NetworkCycleError is thrown by build() if the graph contains a directed cycle.

---

Component 6 — variant_node.hpp: Runtime-typed Node (Python graphs)

Motivation

Python graphs cannot use compile-time type resolution. A PyNetwork is constructed with a closed list of C++ node types known at binding time. The library derives a deduplicated std::variant from all port types across those nodes. Type safety is enforced at connect() time via string signatures.

Variant Deduplication

All port types from the registered nodes are collected into a flat pack, duplicates are removed via a unique_types TMP metafunction, then the variant is instantiated once:

template<typename... Nodes>
using py_variant_t = std::variant<unique_types_t<all_port_types_t<Nodes...>>>;

This is pure TMP and runs entirely at compile time. The resulting variant has no redundant alternatives at runtime.

PyNetwork Construction

make_py_network is a pure C++ template — no CMake code-gen step. The variant is derived entirely at compile time from the registered node type list. The nanobind module definition is the single place where node types are listed; recompiling the extension is the "registration" step.

// In kpn_python.cpp — list all node types that may appear in Python graphs:
auto py_net = make_py_network<NodeA, NodeB, NodeC>();
//   VariantValue = std::variant< /* deduplicated port types from A, B, C */ >
// Registers to_python / from_python converters for each alternative.

VariantChannel

using VariantValue = py_variant_t</* registered nodes */>;

class VariantChannel {
public:
    explicit VariantChannel(std::size_t capacity = 5);
    void push(VariantValue v);   // throws ChannelOverflowError if full
    VariantValue pop();          // blocks (KPN semantics)
};

VariantNode

Wraps a registered C++ node type. Its run_loop uses std::visit to extract the concrete type from a VariantValue, calls the underlying function, then wraps the result back into a VariantValue for the output channel.

class VariantNode {
public:
    std::string input_type_sig(std::size_t idx) const;
    std::string output_type_sig(std::size_t idx) const;

    void connect_input (std::size_t port, std::shared_ptr<VariantChannel>);
    void connect_output(std::size_t port, std::shared_ptr<VariantChannel>);

    void start();
    void stop();
};

PythonConverter — Crossing the C++/Python Boundary

Every type in the variant must provide a PythonConverter specialisation. This is the single mechanism used for all data crossing into or out of Python (PyNodes, net.read, net.write):

template<typename T>
struct PythonConverter {
    static nanobind::object to_python(const T&);
    static T                from_python(nanobind::object);
};

PyNode — Pure Python Processing Node

A PyNode holds a nanobind::object as its function. Its run_loop:

1. Pops VariantValue from each input channel
2. std::visit → calls PythonConverter<T>::to_python for each → acquires GIL
3. Calls the Python callable
4. Releases GIL → calls PythonConverter<R>::from_python on the return value
5. Pushes result as VariantValue to output channel

Sub-value Extraction and Injection

A C++ node returning std::tuple<A, B, C> exposes three independent output ports. Each element is pushed to its own VariantChannel — sub-indexing is a first-class concept at the channel level, not an afterthought.

Python tap — read one output port into Python:

value = net.read("detect", output=2)
# Pops from output channel 2, calls PythonConverter<C>::to_python.
# GIL released while blocking on pop(), re-acquired before to_python call.

Python inject — write a Python value into a specific input port:

net.write("blur", input=1, value=my_sigma)
# Calls PythonConverter<float>::from_python(my_sigma), pushes to input channel 1.
# GIL released while blocking on push() if channel is full.

Python splitter node:

def split(packed):
    img, mask, score = packed
    return img, mask

net.add_node("split", split, inputs=["packed"], outputs=["img", "mask"])
net.connect("detect", 0, "split",  0)
net.connect("split",  0, "show",   0)
net.connect("split",  1, "save",   0)

Direct C++ sub-output to Python node input:

net.connect("detect", 1, "py_thresh", 0)
# Type sig of detect:output[1] must match py_thresh:input[0] — checked at connect().

Type-check at connect time (Python)

# Raises kpn.TypeError if signatures don't match
net.connect("blur", 0, "thresh", 0)   # (src_name, out_idx, dst_name, in_idx)

---

Component 7 — Orchestrator / Watchdog

Runs in its own dedicated thread inside Network / PyNetwork. Responsibilities:

• Starts nodes in topological order; stops them in reverse order
• Tracks per-node execution time (exponential moving average)
• Emits warning via logger callback (default: stderr) when a node stalls beyond threshold
• Catches exceptions from node threads and routes them to ErrorHandler
• Graceful shutdown: signals all nodes, joins with timeout, reports any that fail to stop

---

GIL Rules (non-negotiable constraints on binding implementation)

Two rules govern all interaction between node threads and the Python interpreter:

1. Acquire for callback — a node thread must hold the GIL only for the duration of a Python callable invocation (nb::gil_scoped_acquire wrapping the call site).

2. Release while blocking — any blocking operation on a channel (pop(), push(), net.read(), net.write()) must release the GIL before blocking (nb::gil_scoped_release wrapping the call site), then re-acquire after.

Violating rule 2 deadlocks: a PyNode thread waiting to acquire the GIL cannot proceed while another thread holds the GIL and blocks on a channel waiting for that PyNode to produce.

---

Error Handling Contract

Situation	Behaviour
FIFO overflow	ChannelOverflowError thrown in producer thread → ErrorHandler
Node function throws	Exception pointer captured → ErrorHandler
Type mismatch (C++)	static_assert at connect() compile time
Type mismatch (Python)	kpn.TypeError raised at net.connect() call
Cycle in graph	NetworkCycleError thrown at build() time
Thread fails to stop	Watchdog warning after configurable timeout
from_python / to_python fails	Exception propagated to ErrorHandler

---

Future Extension Points (Heterogeneous Execution)

Not implemented now, but the design must not close these doors:

• IChannel abstract interface — Channel<T> and a future RemoteChannel<T> (wrapping a socket/queue) would share the same push/pop interface. Nodes never know whether their channel is in-process or remote.

• Serializer<T> trait — parallel to PythonConverter<T> and channel_storage_policy, a specialisable trait for cross-device serialisation (MessagePack for ESP32, pinned memory for GPU zero-copy, etc.).

• NodeKind tag — enum class NodeKind { Local, Gpu, Remote } on the INode interface, letting the watchdog apply different health-check and timeout strategies per device type.

These three extension points are sufficient to support GPU and embedded/network targets without redesigning the core.

---

Thread Model

v1: one std::thread per node. This maps directly to KPN theory and is simple to reason about. It does not scale to networks with hundreds of nodes but is appropriate for the typical use case (tens of nodes, each doing non-trivial work).

std::jthread (C++20) is preferred over std::thread where available, as it provides a built-in stop_token that simplifies the stop() / try_pop shutdown pattern.

A future executor/thread-pool model (where multiple nodes share a pool of threads and are scheduled cooperatively) is a possible v2 extension. The INode interface is designed to not assume a 1:1 thread mapping.

---

Platform and Compiler Requirements

C++20 is required. Specific features used:

Feature	Header / Standard	Min compiler
NTTP structural types (fixed_string)	language	GCC 11, Clang 13, MSVC 19.29
std::is_trivially_copyable_v	<type_traits>	C++17+
std::jthread + stop_token	<thread>	GCC 11, Clang 14, MSVC 19.29
if constexpr, fold expressions	language	C++17+
auto NTTPs	language	C++20
Concepts (requires)	language	GCC 10, Clang 10

Minimum supported compilers: GCC 11, Clang 13, MSVC 19.29 (VS 2022). nanobind requires Python 3.8+ and a C++17-capable compiler (satisfied by the above).

---

Testing Strategy

Test frameworks: Catch2 v3 (header-friendly, good async/threading support via REQUIRE_NOTHROW + thread join patterns) and Google Test (for death tests and parameterised test suites). Both are included; use Catch2 for integration/behaviour tests and GTest for unit tests where ASSERT_* / EXPECT_* macros and death tests are preferable.

Hard cases to cover explicitly:

Case	What to test
Channel blocking	pop() blocks until a producer pushes; unblocks exactly once per push
Channel overflow	push() beyond capacity throws ChannelOverflowError
Shutdown race	stop() called while a node is blocked on pop() — thread must exit cleanly
Multi-consumer	Two nodes connected to the same output channel each receive every item (fan-out)
Tuple unpacking	Multi-output node pushes correct type to each sub-channel
Cycle detection	build() throws NetworkCycleError for a graph with a cycle
Named port lookup	input<"wrong">() fires static_assert; input<"right">() resolves correctly
Wrong name count	make_node with mismatched name count fires readable static_assert
GIL deadlock	PyNode + blocking net.read() from Python do not deadlock
from_python failure	Exception propagates to ErrorHandler, network continues
channel_storage_policy	Large type is stored as shared_ptr<const T>; small type by value

---

Examples

Each example is a self-contained program under examples/. They are built as part of the CMake build and serve as both documentation and smoke tests.

examples/01_hello_pipeline — Basic linear pipeline

Two nodes connected in sequence. Demonstrates make_node, Network builder, index-based port connection, start_all / stop_all.

// producer → transform → sink
int produce()               { return 42; }
int double_it(int x)        { return x * 2; }
void print_it(int x)        { std::cout << x << '\n'; }

auto src  = make_node<produce>(5);
auto dbl  = make_node<double_it>(5);
auto sink = make_node<print_it>(5);

Network net;
net.add("src",  src)
   .add("dbl",  dbl)
   .add("sink", sink)
   .connect("src",  src.output<0>(),  "dbl",  dbl.input<0>())
   .connect("dbl",  dbl.output<0>(),  "sink", sink.input<0>())
   .build();
net.start_all();

examples/02_named_ports — Named port access

Same pipeline but using in<> / out<> name tags and named port access. Demonstrates fixed_string NTTP dispatch and the static_assert on wrong names.

auto dbl = make_node<double_it, in<"value">, out<"result">>(5);
// ...
.connect("src", src.output<0>(), "dbl", dbl.input<"value">())
.connect("dbl", dbl.output<"result">(), "sink", sink.input<0>())

examples/03_multi_output — Tuple-returning node / sub-port routing

A single node returns std::tuple<Image, Mask>. Each output is routed to a different downstream node. Demonstrates tuple normalisation, per-element channel push, and output<1>() sub-indexing.

std::tuple<Image, Mask> detect(Image in) { ... }
void show_image(const Image& img)        { ... }
void save_mask(const Mask& m)            { ... }

// detect:output<0> → show_image, detect:output<1> → save_mask

examples/04_storage_policy — channel_storage_policy specialisation

Shows the default behaviour (large struct stored as shared_ptr<const T>, small int by value) and a user specialisation that overrides the default for a custom type.

struct BigFrame { uint8_t pixels[1920*1080*3]; };
// stored as shared_ptr<const BigFrame> automatically

struct Tiny { float x, y; };  // 8 bytes — by value by default
template<> struct channel_storage_policy<Tiny> { static constexpr bool by_value = true; };

examples/05_error_handling — Overflow and node exceptions

Demonstrates ChannelOverflowError (producer faster than consumer, tiny FIFO), custom ErrorHandler, and a node that throws mid-execution.

net.set_error_handler([](std::string_view name, std::exception_ptr ep) {
    try { std::rethrow_exception(ep); }
    catch (const std::exception& e) {
        std::cerr << "[" << name << "] " << e.what() << '\n';
    }
});

examples/06_watchdog — Orchestrator / watchdog

A node that artificially stalls. Shows watchdog warning emission, configurable interval, and graceful shutdown after a timeout.

net.set_watchdog_interval(std::chrono::milliseconds(200));
// stall_node sleeps for 2s per item — watchdog fires warning after 200ms

examples/07_python_network — PyNetwork with C++ and Python nodes

Python script that imports kpn, registers C++ node types via make_py_network, adds a pure Python processing node, connects them, and runs the graph.

import kpn

net = kpn.make_network([kpn.BlurNode, kpn.DetectNode])

def py_filter(img):
    return img[::2, ::2]   # downsample in Python

net.add_node("blur",      kpn.BlurNode,   inputs=["img"])
net.add_node("downsample",py_filter,      inputs=["img"], outputs=["img"])
net.add_node("detect",    kpn.DetectNode, inputs=["img"])
net.connect("blur", 0, "downsample", 0)
net.connect("downsample", 0, "detect", 0)
net.start()

examples/09_opencv_cellshade — Real-time cell-shading with OpenCV (optional)

Captures live video from a system camera and applies a cell-shading effect entirely inside a KPN++ graph. Built only when OpenCV is found at CMake time; skipped silently otherwise.

Graph topology:

[capture] ──Mat──> [split] ──B──> [median_b] ──B──┐
                         ├──G──> [median_g] ──G──┤
                         └──R──> [median_r] ──R──┴──> [merge] ──Mat──> [combine] ──> [display]
[capture] ──Mat──────────────────────> [detect_edges] ──mask──────────> [combine]

Effect steps:

1. split_channels — cv::split into three single-channel cv::Mat planes.
2. median_b/g/r — independent cv::medianBlur(kernel=15) per channel; large kernel posterises colours into flat cartoon-like regions and runs in parallel across channels.
3. merge_channels — cv::merge back to BGR.
4. detect_edges — greyscale, cv::Canny, then cv::dilate to produce thick outlines.
5. combine — zeros out BGR pixels wherever the edge mask is non-zero → black outlines drawn over the flat-colour image.
6. display — cv::imshow; ESC key signals shutdown via g_running atomic.

Demonstrates: named ports, fan-out from a single node to two downstream paths, parallel per-channel processing, multi-input combine node, and error handler driving graceful stop.

// Build only if OpenCV is present:
// cmake .. -DKPN_BUILD_EXAMPLES=ON
// ./09_opencv_cellshade [camera_index]   # default: 0

examples/08_python_subport — Python sub-value tap and inject

Shows net.read("node", output=N) and net.write("node", input=N, value=v) from Python, plus connecting a C++ tuple output sub-port directly to a Python node input.

# Tap only output<1> (Mask) of a C++ detect node into Python
net.connect("detect", 1, "py_thresh", 0)
val = net.read("detect", output=0)   # blocks until Image is available
net.write("blur", input=1, value=1.5)  # inject sigma

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Component 8 — Web Debug UI (optional, compile-time toggle)

An optional in-process HTTP server that serves a live graph visualisation of the running network. Zero cost when disabled — no symbols compiled in, no headers pulled.

Toggle

// Before any kpn include — enables the web debug server
#define KPN_WEB_DEBUG 1
#include <kpn/kpn.hpp>

CMake projects that want it globally:

option(KPN_WEB_DEBUG "Enable KPN++ web debug UI" OFF)
if(KPN_WEB_DEBUG)
    target_compile_definitions(my_app PRIVATE KPN_WEB_DEBUG=1)
    # cpp-httplib is fetched automatically by CMake when this flag is ON
endif()

Implementation

include/kpn/web_debug.hpp — included by network.hpp only when KPN_WEB_DEBUG is defined.

Depends on cpp-httplib (single-header, no external process, no Python required). Served on localhost:9090 by default (configurable via net.set_web_debug_port(uint16_t)).

When enabled, Network gains:

#ifdef KPN_WEB_DEBUG
void set_web_debug_port(uint16_t port);   // default 9090
void start_web_debug();                   // called internally by start()
void stop_web_debug();                    // called internally by stop()
#endif

start() automatically calls start_web_debug() when KPN_WEB_DEBUG is defined.

Endpoints

Endpoint	Method	Description
/	GET	Serves the single-page HTML app (inline, no files needed)
/api/snapshot	GET	Returns a JSON snapshot of all node and channel stats

The HTML page is embedded as a C++ string literal — no asset files to deploy.

JSON Snapshot Format

{
  "nodes": [
    { "id": "src",  "frames": 120, "ema_exec_ms": 33.2, "max_exec_ms": 45.1,
      "blocked_ms": 0.1, "fps": 29.8 },
    { "id": "quant", "frames": 120, "ema_exec_ms": 4.1, ... }
  ],
  "edges": [
    { "source": "src", "target": "quant", "label": "colour",
      "fill_pct": 12.5, "peak_pct": 87.5, "capacity": 8, "current": 1,
      "pushes": 120, "drops": 0, "overflows": 0 }
  ]
}

Node id comes from the name registered via net.add("name", node). Edge label comes from the channel name registered via connect() (format: "src:N → dst:M").

Browser UI

The page polls /api/snapshot every 500 ms and renders a D3.js v7 force-directed graph:

• Nodes — circles labelled with node name; colour encodes exec load:
  • green (ema_exec_ms < 10ms), yellow (10–50ms), orange (50–100ms), red (>100ms)
  • hover tooltip shows: frames, ema_exec_ms, max_exec_ms, blocked_ms, fps
• Edges — directed arrows labelled with the channel name and fill%; colour:
  • green (fill < 50%), yellow (50–80%), red (≥80%) — matches the <<< flag in the text report
  • hover tooltip shows: pushes, drops, overflows, capacity

D3 is loaded from CDN (d3js.org). The entire UI is a single inline HTML string in web_debug.hpp — no file serving, no build step for assets.

Thread model

start_web_debug() launches a std::jthread running httplib::Server::listen(). The server is stopped via httplib::Server::stop() called from stop_web_debug(). /api/snapshot calls collect_snapshots() (already thread-safe — reads atomics with relaxed ordering) and serialises to JSON using a minimal hand-rolled serialiser (no third-party JSON library required).

Example usage

#define KPN_WEB_DEBUG 1
#include <kpn/kpn.hpp>

// ... build network as normal ...
net.set_web_debug_port(9090);  // optional, 9090 is the default
net.start();
// Open http://localhost:9090 in a browser

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CMake Layout

Target	Type	Notes
kpn	header-only interface library	C++20, no external deps
kpn_python	nanobind shared library	links kpn, requires Python 3.8+
kpn_tests	executable	Catch2 v3 + Google Test
kpn_examples	executables (one per example)	built by default, off with -DKPN_EXAMPLES=OFF
kpn_web_debug	compile-time option	#define KPN_WEB_DEBUG 1; fetches cpp-httplib via CMake FetchContent

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Resolved Design Decisions

All major design questions are now closed:

Question	Decision
Shutdown mechanism	accepting_ flag per channel; disable() clears queue and unblocks pop()
Overflow behaviour	ChannelOverflowError thrown on full accepting channel; silently dropped on disabled channel
Network ownership	Non-owning; user declares nodes, network holds raw pointers
Node lifetime contract	Nodes must outlive their Network; declare in same scope
Sub-networks	Network implements INode; expose_input/expose_output define boundary ports
make_py_network	Pure C++ template; nanobind module recompilation is the registration step
GIL strategy	Acquire per Python callback; release while blocking on channel ops