juniper-ml 0.5.0

Juniper - Cascade Correlation Neural Network Research Platform

Juniper: Dynamic Neural Network Research Platform

Juniper is an AI/ML research platform for investigating dynamic neural network architectures and novel learning paradigms. The project emphasizes ground-up implementations from primary literature, enabling a more transparent exploration of fundamental algorithms.

Juniper ML

juniper-ml is the public face of the Juniper platform on PyPI. It is the meta-distribution that aggregates the platform's services (juniper-canopy, juniper-cascor, juniper-data), client libraries (juniper-data-client, juniper-cascor-client), distributed worker (juniper-cascor-worker), and shared tooling (juniper-ci-tools, juniper-doc-tools, juniper-observability) behind a single installation entry point. The package additionally serves as the version-anchor for cross-component compatibility, the home of the platform-level documentation index, and the host repository from which the independently-tagged juniper-observability and juniper-doc-tools packages are published. External callers who want to interact with Juniper services from their own Python code should start here.

Distribution

juniper-ml is published on PyPI as juniper-ml. It provides a single installation entry point for the platform's client stack:

pip install juniper-ml[all]

Individual components — juniper-canopy, juniper-cascor, juniper-data, juniper-data-client, juniper-cascor-client, juniper-cascor-worker, juniper-ci-tools, juniper-doc-tools, and juniper-observability — remain installable in isolation for callers that require finer control over their dependency surface.

Ecosystem Compatibility

This meta-package is part of the Juniper ecosystem. The pyproject pins matching juniper-ml 0.5.0:

Package	Pin	Latest published
juniper-canopy	>=0.3.0	0.3.0
juniper-cascor	>=0.3.17	0.3.17
juniper-data	>=0.6.0	0.6.0
juniper-data-client	>=0.4.0	0.4.0
juniper-cascor-client	>=0.3.0	0.3.0
juniper-cascor-worker	>=0.3.0	0.3.0
juniper-ci-tools	>=0.1.0	0.1.0
juniper-doc-tools	>=0.1.0,<0.2.0	0.1.1
juniper-observability	>=0.2.0	0.2.0

For full-stack Docker deployment and integration tests, see juniper-deploy.

Architecture

juniper-ml is a meta-package: it ships no importable code of its own. Its role is to declare, version, and install the libraries through which external callers interact with the platform's services. The diagram below reproduces the platform's dependency graph from the parent ecosystem documentation; juniper-ml sits at the bottom-right, aggregating the client side.

juniper-cascor ──uses──> juniper-data-client ──calls──> juniper-data
juniper-cascor ──managed by──> juniper-cascor-worker (distributed training, architectural only — no code import dependency)
juniper-cascor-client ──calls──> juniper-cascor (REST/WebSocket)
juniper-canopy ──uses──> juniper-data-client ──calls──> juniper-data
juniper-canopy ──uses──> juniper-cascor-client ──calls──> juniper-cascor
juniper-deploy ──orchestrates──> juniper-data, juniper-cascor, juniper-canopy (Docker)
juniper-ml ──meta-package──> juniper-canopy, juniper-cascor, juniper-data,
                              juniper-data-client, juniper-cascor-client, juniper-cascor-worker,
                              juniper-ci-tools, juniper-doc-tools, juniper-observability
juniper-ml ──hosts (independently published)──> juniper-observability, juniper-doc-tools

Related Services

Component	Relationship	Notes
juniper-canopy	Aggregated under [servers] and [all]	pip install juniper-canopy
juniper-cascor	Aggregated under [servers] and [all]	pip install juniper-cascor
juniper-data	Aggregated under [servers] and [all]	pip install juniper-data
juniper-data-client	Aggregated under [clients] and [all]	pip install juniper-data-client
juniper-cascor-client	Aggregated under [clients] and [all]	pip install juniper-cascor-client
juniper-cascor-worker	Aggregated under [worker] and [all]	pip install juniper-cascor-worker
juniper-ci-tools	Aggregated under [tools] and [all]; published from this repository	pip install juniper-ci-tools
juniper-doc-tools	Aggregated under [doc-tools], [tools], and [all]; published from this repository	pip install juniper-doc-tools
juniper-observability	Aggregated under [tools] and [all]; published independently from this repository	pip install "juniper-observability[all]"

Extras

Extra	Packages Included
clients	juniper-data-client>=0.4.0, juniper-cascor-client>=0.3.0
worker	juniper-cascor-worker>=0.3.0
servers	juniper-canopy>=0.3.0, juniper-cascor>=0.3.17, juniper-data>=0.6.0
tools	juniper-ci-tools>=0.1.0, juniper-doc-tools>=0.1.0,<0.2.0, juniper-observability>=0.2.0
doc-tools	juniper-doc-tools>=0.1.0,<0.2.0 (back-compat alias for the doc-tools entry in tools)
all	All of the above

Active Research Components

The active research components of the Juniper platform are surfaced through the component repositories listed below. Each repository's README documents the algorithms, datasets, protocols, or operational primitives it implements; juniper-ml aggregates their client surfaces and does not host research code of its own.

Component	Research artifact
juniper-cascor	Cascade-Correlation reference implementation (Fahlman & Lebiere, 1990), candidate-pool training protocol, multi-network orchestration
juniper-data	Dataset-generation service, named-version registry, ARC-AGI dataset families
juniper-canopy	Real-time training-dynamics visualisation, network-topology renderer, WebSocket control surface
juniper-cascor-worker	Distributed candidate-unit training over a WebSocket worker protocol
juniper-cascor-client	REST + WebSocket training-stream and control-stream client protocols
juniper-observability	Idempotent Prometheus collector helpers (register_or_reuse family), structured-JSON logging, Starlette middleware
juniper-doc-tools	juniper-check-doc-links CLI for cross-repo and ecosystem-root markdown link validation

Quick Start Guide

Prerequisites

• Python ≥ 3.12

The meta-package itself has no other prerequisites. Component-specific requirements (e.g. a running juniper-data or juniper-cascor service) are documented in the corresponding component repository.

Installation

pip install juniper-ml[all]            # all servers, clients, worker, and tools
pip install juniper-ml[servers]        # juniper-canopy + juniper-cascor + juniper-data
pip install juniper-ml[clients]        # juniper-data-client + juniper-cascor-client
pip install juniper-ml[worker]         # juniper-cascor-worker
pip install juniper-ml[tools]          # juniper-ci-tools + juniper-doc-tools + juniper-observability
pip install juniper-ml[doc-tools]      # juniper-doc-tools only (back-compat alias)

Verification

Confirm the client libraries are importable:

from juniper_data_client import JuniperDataClient
from juniper_cascor_client import JuniperCascorClient

Confirm the documentation tooling CLI is on PATH:

juniper-check-doc-links --version

Next Steps

• docs/QUICK_START.md — installation and verification guide
• docs/REFERENCE.md — extras, compatibility matrix, environment variables, service ports
• juniper-deploy — Docker Compose orchestration for the full-stack platform

Research Philosophy

The Juniper platform exists to study learning algorithms whose network architecture is not fixed in advance. Its initial anchor is the Cascade-Correlation algorithm of Fahlman and Lebiere (1990), implemented from the primary literature without recourse to higher-level abstractions that elide the algorithm's operational detail. The organising commitment is that algorithm implementations remain inspectable at the level at which they were originally specified: candidate units, correlation objectives, weight-freezing semantics, and the structural events that grow the network are first-class artifacts of the codebase rather than internal details of a library wrapper. This permits comparative work — across algorithms, datasets, and hyperparameter regimes — to be conducted on a known and reproducible substrate.

The current platform comprises a Cascade-Correlation training service exposing a REST and WebSocket interface, a dataset-generation service with a named-version registry that includes the ARC-AGI families, a real-time monitoring dashboard for inspecting training dynamics as they occur, and a distributed worker that parallelises candidate-unit training across hosts. Near-term work extends the architectural-growth catalogue beyond Cascade-Correlation, introduces multi-network orchestration for comparative experiments at the level of network populations rather than individual runs, and tightens the dataset–training–monitoring loop into a reproducible research workbench. The longer-term direction is the systematic empirical study of constructive and architecture-growing learning algorithms, with first-class infrastructure for the ablation, comparison, and replication that such a study requires.

Within this programme, juniper-ml is the integration surface: a single installation entry point that aggregates the client libraries needed to interact with the platform from external Python code, and a version-anchor that makes the compatibility of components legible at a glance.

Documentation

Document	Purpose
docs/DOCUMENTATION_OVERVIEW.md	Navigation index for all juniper-ml documentation
docs/QUICK_START.md	Installation and verification guide
docs/REFERENCE.md	Extras, compatibility matrix, environment variables, service ports
docs/DEVELOPER_CHEATSHEET_JUNIPER-ML.md	Quick-reference card for development tasks
notes/README_NORMALIZATION_PLAN_2026-05-19.md	Ecosystem-wide README normalization plan (this README is its reference implementation)
notes/RESEARCH_PHILOSOPHY_CANONICAL_DRAFT_2026-05-19.md	Source-of-truth for the Research Philosophy text inlined above

License

MIT License — Copyright (c) 2024-2026 Paul Calnon