dtlpy 1.114.17

SDK and CLI for Dataloop platform

DTLPY – SDK and CLI for Dataloop.ai

📚 Platform Documentation | 📖 SDK Documentation | Developer docs

An open-source SDK and CLI toolkit to interact seamlessly with the Dataloop.ai platform, providing powerful data management, annotation capabilities, and workflow automation.

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Table of Contents

• Overview
• Installation
• Usage
  • SDK Usage
  • CLI Usage
• Python Version Support
• Development
• Resources
• Contribution Guidelines

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Overview

DTLPY provides a robust Python SDK and a powerful CLI, enabling developers and data scientists to automate tasks, manage datasets, annotations, and streamline workflows within the Dataloop platform.

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Installation

Install DTLPY directly from PyPI using pip:

pip install dtlpy

Alternatively, for the latest development version, install directly from GitHub:

pip install git+https://github.com/dataloop-ai/dtlpy.git

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Usage

SDK Usage

Here's a basic example to get started with the DTLPY SDK:

import dtlpy as dl

# Authenticate
dl.login()

# Access a project
project = dl.projects.get(project_name='your-project-name')

# Access dataset
dataset = project.datasets.get(dataset_name='your-dataset-name')

CLI Usage

DTLPY also provides a convenient command-line interface:

dlp login
dlp projects ls
dlp datasets ls --project-name your-project-name

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Python Version Support

DTLPY supports multiple Python versions as follows:

Python Version	3.11	3.10	3.9	3.8	3.7	3.6	3.5
dtlpy >= 1.99	✅	✅	✅	✅	✅	❌	❌
dtlpy 1.76–1.98	✅	✅	✅	✅	✅	✅	❌
dtlpy >= 1.61	❌	✅	✅	✅	✅	✅	❌
dtlpy 1.50–1.60	❌	❌	✅	✅	✅	✅	❌
dtlpy <= 1.49	❌	❌	✅	✅	✅	✅	✅

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Development

To set up the development environment, clone the repository and install dependencies:

git clone https://github.com/dataloop-ai/dtlpy.git
cd dtlpy
pip install -r requirements.txt

Resources

• Dataloop Platform
• Full SDK Documentation
• Platform Documentation
• SDK Examples and Tutorials
• Developer docs

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Contribution Guidelines

We encourage contributions! Please ensure:

• Clear and descriptive commit messages
• Code follows existing formatting and conventions
• Comprehensive tests for new features or bug fixes
• Updates to documentation if relevant

Create pull requests for review. All contributions will be reviewed carefully and integrated accordingly.