loop-node

loop-node is the low-level runtime and wire protocol shared by Loop and loop-sdk. Most integrations should use loop-sdk directly; use loop-node when building a lower-level Node API or integration layer.

Installation

pip install loop-node

Published wheels support CPython 3.10–3.14 on Linux x86_64 (glibc 2.28+). They contain compiled implementation modules and type stubs; installation does not require a compiler.

Minimal example

import time

from loop_node import (
    EmptyConfig,
    FieldContract,
    LifecycleState,
    Node,
    NodeConnectionConfig,
    PayloadContract,
    ValueKind,
)

VALUE = PayloadContract(fields={"value": FieldContract(kind=ValueKind.SCALAR)})

node = Node(config_type=EmptyConfig)
output = node.declare_stream_output("value", payload_contract=VALUE)
node.start(
    node_id="example_node",
    connection=NodeConnectionConfig(loop_endpoint="tcp/127.0.0.1:7448"),
)

try:
    while node.is_running:
        node.process_control()
        if node.status.lifecycle is LifecycleState.ACTIVE:
            output.publish(timestamp_ns=time.time_ns(), payload={"value": 1.0})
        time.sleep(0.1)
finally:
    if node.status.lifecycle is not LifecycleState.FINALIZED:
        node.shutdown()
    node.close()

A Node declares its Config and Ports before connecting. Loop supplies the validated Config, Port bindings, and lifecycle operations. Call process_control() regularly to handle lifecycle requests from Loop. Use the Node lifecycle state to decide when your application should exchange Graph data. In most cases, only exchange data while the Node is ACTIVE.

Lifecycle

All Nodes use IDLE → Configure → CONFIGURED → Start → ACTIVE. Configure validates Config and calls optional on_configure(config) before Port binding. Start takes only bindings and calls optional on_start(). Stop and ResetFault return to IDLE; each restart requires Configure again. Configure may update existing payload contracts. The Orchestrator validates the resulting graph before starting Nodes. Port names and kinds stay fixed.

Describe returns the Config schema and current Port contracts for inspection. Loop uses Configure results for payload compatibility checks; contracts read through Describe are not treated as finalized for a run.

Shared-memory streams

Use the shared_memory communication profile for Stream ports between Nodes on the same machine. It supports every payload value type: scalars, booleans, strings, tensors, images, and audio. A message may contain multiple binary fields, optional fields, or no binary fields. Logical types and payload contract validation still apply. Request/Reply continues to use protobuf.

In a Cell Config, select communication_profile: shared_memory on the consumer binding. In a low-level Graph Config, define a bounded profile:

communication_profiles:
  local_shared:
    transport: zenoh
    stream_mode: shared_memory
    shared_memory_pool_size_bytes: 67108864

Reference local_shared from the consumer binding. The pool limit must cover all binary fields in a message and messages retained by subscribers, with room for allocation overhead. Allocation exhaustion raises an error; the transport does not silently fall back to ordinary bytes. Profile selection is explicit; this change does not automatically switch small messages to SHM.

The wire format has a Protobuf metadata attachment and one contiguous SHM allocation. Each image, tensor, or audio field has an offset and byte length in that allocation. Binary bytes are copied directly into their destination ranges without first building a combined Protobuf byte string. Scalar fields remain in the attachment. Messages with no binary bytes use one padding byte because Zenoh requires a nonempty allocation; SHM offers no bulk-copy advantage for those messages.

The receiver validates the message and all buffer references, layout, and sizes. Image/audio buffers retain the original Zenoh Sample, including after the subscriber closes. Zenoh Python 1.9.0 does not expose received SHM through the Python buffer protocol: packed ranges share a lazy Python snapshot, and tensor decoding materializes that snapshot to preserve TensorValue.data as a read-only memoryview. This is not end-to-end zero-copy NumPy access.

Key dates

PyPI data
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  • Released:
Newer release available (0.3.0)

1 maintainer

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Avatar for chanyoungkim-configinc from gravatar.com chanyoungkim-configinc

Requires

Python >=3.10

Provides Extra

numpy

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