namaka 0.1.2

Namaka Python SDK for experiment tracking

Namaka Python SDK

Lightweight Python client for Namaka experiment tracking. Logs metrics in the background so it never blocks your training loop.

Installation

pip install namaka

Or for development:

cd sdk
pip install -e .

Quick Start

import namaka

run = namaka.init(
    project="my-project",
    api_url="http://localhost:8000/api/v1",
)

# Log hyperparameters
run["parameters"] = {
    "learning_rate": 0.001,
    "batch_size": 64,
    "model_type": "transformer",
}

# Log metrics during training
for epoch in range(100):
    loss = train_step()
    accuracy = evaluate()
    run.log("train/loss", loss, step=epoch)
    run.log("val/accuracy", accuracy, step=epoch)

run.stop()

Connecting to Render

run = namaka.init(
    project="my-project",
    api_url="https://namaka-backend.onrender.com/api/v1",
)

API Reference

namaka.init(project, api_url, name, api_token)

Creates a new run and returns a Run object. Creates the project if it doesn't exist.

Parameter	Type	Default	Description
project	str	required	Project name (or "workspace/project" format)
api_url	str	http://localhost:8000/api/v1	Backend API base URL
name	str | None	None	Optional run name
api_token	str | None	None	Reserved for future authentication

run.log(name, value, step=None)

Buffer a metric data point. Flushed to the backend every 5 seconds by a background thread.

run["key"] = {...}

Set run parameters via dict assignment. Values are type-inferred (int, float, bool, string).

run["parameters"] = {"lr": 0.001, "epochs": 10}
run["system"] = {"gpu": "A100", "framework": "pytorch"}

run.stop()

Flush remaining data and mark the run as completed. Called automatically via atexit if not called explicitly.

run.id

The integer run ID assigned by the backend.

How It Works

• Metrics and params are collected in thread-safe buffers
• A background daemon thread flushes every 5 seconds and sends heartbeats
• Failed flushes re-add data to the buffer for retry
• Requires Python 3.10+ and httpx