tyko 0.1.8

Official Python SDK for Tyko Labs - Track experiments, manage models, and version datasets

Tyko Client - Python SDK

Official Python SDK for Tyko Labs.

Track experiments, manage models, and version datasets with a simple, intuitive API.

Hierarchy

Tyko uses a three-level hierarchy to organize your ML work:

Project → Experiment → Run

• Project: Top-level container for your ML project (e.g., "mnist-classifier")
• Experiment: Groups related runs for comparison (e.g., "hyperparameter-search")
• Run: A single training execution with parameters, metrics, and artifacts

Installation

Install via pip:

pip install tyko

Quick Start

from tyko import TykoClient

client = TykoClient()

# Simplest usage - just project name (uses "default" experiment)
# Environment info (Python version, CPU, GPU, etc.) is auto-captured
with client.start_run(project="my-ml-project") as run:
    run.params["learning_rate"] = 0.001
    run.params["batch_size"] = 32
    # ... your training code ...

# With params at creation time
with client.start_run(
    project="my-ml-project",
    experiment="hyperparameter-search",
    params={"learning_rate": 0.01, "batch_size": 64}
) as run:
    # Params are already set, can add more during the run
    run.params["epochs"] = 100
    # ... your training code ...

Environment Capture

Environment information is automatically captured when you start a run:

• Python version
• Operating system/platform
• CPU count
• RAM size (if psutil is installed)
• GPU count and names (if torch is available)

You can also manually add environment details:

with client.start_run(project="ml-experiments") as run:
    # Add custom environment info
    run.environment["git_commit"] = "abc123"
    run.environment["cuda_version"] = "12.1"

To use the standalone function:

from tyko import capture_environment

env = capture_environment()
print(env)  # {'python_version': '3.12.1', 'platform': 'Linux-...', ...}