juniper-cascor-client 0.3.0

HTTP/WebSocket client for the JuniperCascor neural network training service

juniper-cascor-client

Python HTTP/WebSocket client for the JuniperCascor cascade correlation neural network training service.

Ecosystem Compatibility

This package is part of the Juniper ecosystem. Compatible with:

juniper-data	juniper-cascor	juniper-canopy
0.4.x	0.3.x	0.2.x

Installation

pip install juniper-cascor-client

Usage

REST Client

from juniper_cascor_client import JuniperCascorClient

with JuniperCascorClient("http://localhost:8200") as client:
    # Create a network
    client.create_network(input_size=2, output_size=2, learning_rate=0.01)

    # Start training
    client.start_training(
        dataset={"source": "inline"},
        inline_data={
            "train_x": [[0, 0], [1, 0], [0, 1], [1, 1]],
            "train_y": [[1, 0], [0, 1], [0, 1], [1, 0]],
        },
        epochs=100,
    )

    # Check status
    status = client.get_training_status()
    print(status["data"]["training_active"])

    # Get metrics
    metrics = client.get_metrics()
    print(f"Loss: {metrics['data']['train_loss']}")

WebSocket Training Stream

import asyncio
from juniper_cascor_client import CascorTrainingStream

async def monitor():
    async with CascorTrainingStream("ws://localhost:8200") as stream:
        async for message in stream:
            if message["type"] == "metrics":
                print(f"Epoch {message['data']['epoch']}: loss={message['data']['train_loss']}")
            elif message["type"] == "cascade_add":
                print(f"New hidden unit added!")

asyncio.run(monitor())

WebSocket Control

import asyncio
from juniper_cascor_client import CascorControlStream

async def control():
    async with CascorControlStream("ws://localhost:8200") as ctrl:
        result = await ctrl.command("start", {"epochs": 200})
        print(result)

asyncio.run(control())

API Reference

JuniperCascorClient

Method	Description
health_check()	Service health check
is_alive()	Liveness probe
is_ready()	Readiness probe
wait_for_ready(timeout)	Wait for service readiness
create_network(**kwargs)	Create a CasCor network
get_network()	Get network state
delete_network()	Destroy network
get_topology()	Network topology for visualization
get_statistics()	Network weight statistics
start_training(...)	Start async training
stop_training()	Stop training
pause_training()	Pause training
resume_training()	Resume training
reset_training()	Reset state
get_training_status()	Current training status
get_training_params()	Training parameters
get_metrics()	Current metrics
get_metrics_history(count)	Metrics history
get_dataset()	Dataset metadata
get_decision_boundary(resolution)	Decision boundary grid

CascorTrainingStream

Async WebSocket client for /ws/training. Supports async iteration and callbacks.

CascorControlStream

Async WebSocket client for /ws/control. Send commands and receive responses.

Juniper Ecosystem

This package is part of the Juniper Cascade Correlation Neural Network Research Platform.

Package	Description	Install
juniper-data-client	Dataset service client	pip install juniper-data-client
juniper-cascor-client	Neural network service client (this package)	pip install juniper-cascor-client
juniper-cascor-worker	Distributed training worker	pip install juniper-cascor-worker

License

MIT License - see LICENSE for details.