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.
Table of Contents
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.
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
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
Python Version Support
DTLPY supports multiple Python versions as follows:
| Python Version | 3.14 | 3.13 | 3.12 | 3.11 | 3.10 | 3.9 | 3.8 | 3.7 |
|---|---|---|---|---|---|---|---|---|
| dtlpy >= 1.118 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| dtlpy 1.99–1.117 | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
| dtlpy 1.76–1.98 | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
| dtlpy >= 1.61 | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |
| dtlpy 1.50–1.60 | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
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
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.
4 maintainers
Metadata
4 maintainers
Release files for dtlpy 1.126.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dtlpy-1.126.5.tar.gz | 519.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dtlpy-1.126.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / dtlpy-1.126.5.tar.gz
| Download URL | dtlpy-1.126.5.tar.gz |
|---|---|
| Size | 519.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
099a691837e45475f8eaeb481522304b1c6c98da1a1bd8b1b3898f9f4475f12a
|
|
BLAKE2b-256 checksum How to use checksums |
730017ab17dcb0b7bdf312c286acbe599e36f932b11c795cdc24b0e999df096c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.10.21
|
Release files / dtlpy-1.126.5-py3-none-any.whl
| Download URL | dtlpy-1.126.5-py3-none-any.whl |
|---|---|
| Size | 632.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
490d061bc1d2d5958f8c0f17570a2cc86250e793418a96b7e1c5407d5c52e7a2
|
|
BLAKE2b-256 checksum How to use checksums |
13924d06df1dc49dc400cfb2b1eaaba2db80b3d131ea26b02274529ffd9abd33
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.10.21
|
