Unitlab.ai is an AI-driven data annotation platform that automates the collection of raw data, facilitating collaboration with human annotators to produce highly accurate labels for your machine learning models. With our service, you can optimize work efficiency, improve data quality, and reduce costs.
Unitlab Python SDK
Python SDK and CLI for the Unitlab.ai data annotation platform. Manage projects, upload data, and download datasets programmatically or from the command line.
Installation
pip install --upgrade unitlab
Requires Python 3.10+.
Configuration
Get your API key from unitlab.ai and configure the CLI:
# Set API key
unitlab configure --api-key YOUR_API_KEY
# Set a custom API URL
unitlab configure --api-url https://api.unitlab.ai
# Set both at once
unitlab configure --api-key YOUR_API_KEY --api-url https://api.unitlab.ai
Or set environment variables:
export UNITLAB_API_KEY=YOUR_API_KEY
# Optional: point to a custom API server (e.g. self-hosted)
export UNITLAB_API_URL=https://api.unitlab.ai
Python SDK
from unitlab import UnitlabClient
# Initialize with an explicit key
client = UnitlabClient(api_key="YOUR_API_KEY")
# Or read from UNITLAB_API_KEY env var / config file
client = UnitlabClient()
The client can also be used as a context manager:
with UnitlabClient() as client:
projects = client.projects()
Projects
# List all projects
projects = client.projects()
# Get project details
project = client.project("PROJECT_ID")
Upload data
client.project_upload_data(
project_id="PROJECT_ID",
directory="./images",
)
The upload command accepts mixed directories. File type is detected per file from
the supported extensions: images, text, video, audio, medical
(.dcm, .nii, .nii.gz, .nrrd), and documents (.pdf).
Additional options for specific file types:
# Video files
client.project_upload_data("PROJECT_ID", "./videos", fps=30.0)
# PDF documents
client.project_upload_data("PROJECT_ID", "./pdfs")
# DICOM, NIfTI, and NRRD medical volumes
client.project_upload_data("PROJECT_ID", "./medical")
Datasets
# List all datasets
datasets = client.datasets()
# Download one annotation split using the release's fixed export format
path = client.dataset_download("DATASET_ID", split_type="train")
# Omit split_type to download all available splits as one bundle
path = client.dataset_download("DATASET_ID")
# Download raw files
folder = client.dataset_download_files("DATASET_ID")
CLI
Projects
# List projects
unitlab project list
# Project details
unitlab project detail PROJECT_ID
# Upload data to a project
unitlab project upload PROJECT_ID --directory ./images
# Upload PDFs or medical volumes with the same command
unitlab project upload PROJECT_ID --directory ./pdfs
unitlab project upload PROJECT_ID --directory ./medical
Datasets
# List datasets
unitlab dataset list
# Download one annotation split using the release's fixed export format
unitlab dataset download DATASET_ID --split-type train
# Download all annotation splits as one bundle
unitlab dataset download DATASET_ID
# Download raw files
unitlab dataset download DATASET_ID --download-type files
Documentation
See the full documentation for detailed guides:
License
3 maintainers
Metadata
3 maintainers
Release files for unitlab 2.5.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| unitlab-2.5.1.tar.gz | 12.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| unitlab-2.5.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.9 kB
Release files / unitlab-2.5.1.tar.gz
| Download URL | unitlab-2.5.1.tar.gz |
|---|---|
| Size | 12.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.12
|
Release files / unitlab-2.5.1-py3-none-any.whl
| Download URL | unitlab-2.5.1-py3-none-any.whl |
|---|---|
| Size | 12.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0666fba5d62494c06ecfa1975cbf7fb3129514b3cc66b1f03749399c33218101
|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.12
|
