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

Metadata

Release files for tyko 0.1.50

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tyko 0.1.50
File	Size	Uploaded
tyko-0.1.50.tar.gz	66.9 kB	Jun 20, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for tyko 0.1.50
File	Interpreter	ABI	Platform	Reset
tyko-0.1.50-py3-none-any.whl 46.7 kB Jun 20, 2026	Python 3	none	any	Details

Total release size: 113.6 kB

Release files / tyko-0.1.50.tar.gz

Download URL	tyko-0.1.50.tar.gz
Size	66.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`471a0d9203f46fab22a66a2e40a27a115d6a71c5b372295961207af3825a7a98`
BLAKE2b-256 checksum How to use checksums	`580f79558cd313a0b94fdacab0c6e25b4b58e5ab363bc0cc3d11a96125acc31d`
Upload date	Jun 20, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Hatch/1.17.0 {"ci":true,"cpu":"x86_64","distro":{"id":"bookworm","libc":{"lib":"glibc","version":"2.36"},"name":"Debian GNU/Linux","version":"12"},"implementation":{"name":"CPython","version":"3.14.5"},"installer":{"name":"hatch","version":"1.17.0"},"openssl_version":"OpenSSL 3.5.6 7 Apr 2026","python":"3.14.5","system":{"name":"Linux","release":"6.19.14+deb14-amd64"}} HTTPX2/2.4.0`

Release files / tyko-0.1.50-py3-none-any.whl

Download URL	tyko-0.1.50-py3-none-any.whl
Size	46.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`e797b0aa6e79e1993a113c7f693ba23216bc73520dd9ceafb9fcdce3a3a56d7d`
BLAKE2b-256 checksum How to use checksums	`b01655662a85f35182b251028e71c342ecb4132bb02c1ad5a28358fc69f5bec1`
Upload date	Jun 20, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Hatch/1.17.0 {"ci":true,"cpu":"x86_64","distro":{"id":"bookworm","libc":{"lib":"glibc","version":"2.36"},"name":"Debian GNU/Linux","version":"12"},"implementation":{"name":"CPython","version":"3.14.5"},"installer":{"name":"hatch","version":"1.17.0"},"openssl_version":"OpenSSL 3.5.6 7 Apr 2026","python":"3.14.5","system":{"name":"Linux","release":"6.19.14+deb14-amd64"}} HTTPX2/2.4.0`