tyko 0.1.49

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-...', ...}

Metric Logging

Log metrics during training using run.log():

with client.start_run(project="image-classifier") as run:
    run.params["learning_rate"] = 0.001
    run.params["batch_size"] = 32

    for epoch in range(100):
        train_loss = train_epoch(model, train_data)
        val_loss, val_acc = evaluate(model, val_data)

        # Log multiple metrics at once
        run.log({
            "train/loss": train_loss,
            "eval/loss": val_loss,
            "eval/accuracy": val_acc,
            "epoch": epoch,
        })

Metric Naming Conventions

Use prefixes with slashes for grouped visualization in the dashboard:

Pattern	Example	Dashboard Grouping
prefix/metric	train/loss, train/accuracy	Grouped under "train"
prefix/metric	eval/loss, eval/accuracy	Grouped under "eval"
Plain name	epoch, step	Ungrouped

This allows the dashboard to organize metrics into logical groups automatically.

Configuration

API Key

Set your API key via environment variable (recommended):

export TYKO_API_KEY="your-api-key"

Or pass it explicitly:

client = TykoClient(api_key="your-api-key")

If the server has anonymous access enabled (ALLOW_ANONYMOUS=true), no API key is needed:

client = TykoClient(api_url="https://your-server.com")

Server URL

For self-hosted deployments, set the server URL:

export TYKO_API_URL="https://your-server.com"

Or:

client = TykoClient(api_url="https://your-server.com")

Development

Prerequisites

• Python 3.11+
• uv package manager

Setup

cd packages/tyko-client
uv sync

Testing

moon run tyko-client:test

Linting

# Check code style
moon run tyko-client:ruff

# Format code
moon run tyko-client:black

# Type checking
moon run tyko-client:mypy

# All three
moon run tyko-client:lint

Building

moon run tyko-client:build