### Start Label Studio application Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/quick_start.md Starts the Label Studio application. After installation, this command launches the web server, making Label Studio accessible via a web browser. ```bash label-studio start ``` -------------------------------- ### Setup Label Studio for Local Development Source: https://github.com/humansignal/label-studio/blob/develop/README.md Configures the environment from source code, runs database migrations, and starts the development server. ```bash pip install poetry poetry install python label_studio/manage.py migrate python label_studio/manage.py collectstatic python label_studio/manage.py runserver ``` -------------------------------- ### Start Hexo Server for Local Deployment Source: https://github.com/humansignal/label-studio/blob/develop/docs/README.md Starts the Hexo static site generator server locally. This command is used after installing dependencies to serve the documentation on your machine. ```shell hexo serve ``` -------------------------------- ### Install Label Studio ML Backend and SAM2 Example Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/segment_anything_2_image.md These bash commands guide the user through cloning the Label Studio ML backend repository, installing its dependencies, and setting up the specific example for SAM2 image segmentation. It also includes installing requirements for the SAM2 example and downloading the SAM2 model checkpoints. ```bash git clone https://github.com/HumanSignal/label-studio-ml-backend.git cd label-studio-ml-backend pip install -e . cd label_studio_ml/examples/segment_anything_2_image pip install -r requirements.txt ``` ```bash cd ../ label-studio-ml start ./segment_anything_2_image ``` -------------------------------- ### Start Development Server with npm Source: https://github.com/humansignal/label-studio/blob/develop/docs/README.md Starts a local development server for the documentation. The server typically runs at http://localhost:4000/, allowing for live preview during development. ```shell npm run server ``` -------------------------------- ### Start Label Studio (Native) Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/start.md This command starts a Label Studio instance using the native installation. It requires setting the LABEL_STUDIO_HOST environment variable to the ngrok URL. Ensure the URL does not have a trailing slash. ```bash # Avoid CSRF errors # Must exactly match the ngrok HTTPS URL, with no trailing slash set CSRF_TRUSTED_ORIGINS=https://your-subdomain.ngrok-free.app # Start Label Studio LABEL_STUDIO_HOST=https://your-subdomain.ngrok-free.app label-studio start ``` -------------------------------- ### Install Label Studio from Source Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/install.md Clones the repository and uses Poetry to manage dependencies, run database migrations, and start the development server. Recommended for contributors or users requiring nightly builds. ```bash git clone https://github.com/HumanSignal/label-studio.git cd label-studio pip install poetry poetry install poetry run python label_studio/manage.py migrate poetry run python label_studio/manage.py collectstatic poetry run python label_studio/manage.py runserver ``` -------------------------------- ### Install Dependencies with npm Source: https://github.com/humansignal/label-studio/blob/develop/docs/README.md Installs the necessary project dependencies using the Node Package Manager (npm). This is a prerequisite for running development servers or building the project. ```shell npm install ``` -------------------------------- ### Install and Setup Label Studio ML Backend Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/yolo.md Commands to install Label Studio and clone the ML backend repository for custom model integration. ```bash pip install label-studio label-studio git clone https://github.com/HumanSignal/label-studio-ml-backend.git cd label-studio-ml-backend/examples/yolo ``` -------------------------------- ### Start Image Bounding Box Labeling Server Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/image_bbox/START.md Launches the Label Studio server with specific configuration, labeling interface, and task input files. It outputs the labeling results to the specified directory. ```bash python server.py -c config.json -l ../examples/image_bbox/config.xml -i ../examples/image_bbox/tasks.json -o output ``` -------------------------------- ### Initialize and Start ML Backend Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/easyocr.md Instructions for setting up a Python virtual environment, installing dependencies, and launching the ML backend service manually. ```bash python -m venv ml-backend source ml-backend/bin/activate pip install -r requirements.txt label-studio-ml start ./easyocr ``` -------------------------------- ### Start Label Studio for Image Bounding Box Labeling Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/image_ellipses/START.md Launches the Label Studio server for image object detection. It specifies the configuration file, task data, and output directory. ```bash python server.py -c config.json -l ../examples/image_ellipses/config.xml -i ../examples/image_ellipses/tasks.json -o output ``` -------------------------------- ### Start Label Studio Server for Image Bounding Box Labeling Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/image_multilabel/START.md Launches the Label Studio server using a configuration file, XML labeling schema, and task data. The output is directed to a specified directory. ```bash python server.py -c config.json -l ../examples/image_multilabel/config.xml -i ../examples/image_multilabel/tasks.json -o output ``` -------------------------------- ### Start ML Backend with Docker Compose Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/bert_classifier.md Starts the Label Studio ML backend using Docker Compose. This is the recommended method for running the backend. It assumes you have Docker and Docker Compose installed. ```bash docker-compose up ``` -------------------------------- ### Start ML Backend for SAM2 Video Annotation (Bash) Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/segment_anything_2_video.md This bash command starts the Label Studio ML backend service for the segment_anything_2_video example. It requires the segment-anything-2 repository to be downloaded and specific environment variables to be set. ```bash cd ../ label-studio-ml start ./segment_anything_2_video ``` -------------------------------- ### Start Audio Video Paragraph Server Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/audio_video_paragraphs/START.md Command to launch the Label Studio server using a specific configuration, XML labeling interface, and input task file. It outputs the results to a designated directory. ```bash python server.py -c config.json -l ../examples/audio_video_paragraph/config.xml -i ../examples/audio_video_paragraph/tasks.json -o output ``` -------------------------------- ### Start the server Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/image_keypoints/START.md Launches the server with the specified configuration, XML labeling config, and task data. ```bash python server.py -c config.json -l ../examples/image_keypoints/config.xml -i ../examples/image_keypoints/tasks.json -o output ``` -------------------------------- ### Initialize and launch ML backend Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/dummy_model.md Command line instructions to initialize the ML backend directory and start the development server. ```bash label-studio-ml init my_backend label-studio-ml start my_backend ``` -------------------------------- ### Start image bounding box labeling server Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/classification_mixed/START.md Execute this command to launch the server with the specified configuration, task, and output files. ```bash python server.py -c config.json -l ../examples/image_bbox/config.xml -i ../examples/image_bbox/tasks.json -o output ``` -------------------------------- ### Start Label Studio Server Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/install_requirements.md Starts the Label Studio server after installation. The server typically runs on http://localhost:8080 and opens automatically in the default web browser. ```bash label-studio ``` -------------------------------- ### Install Dependencies for SAM2 Video Annotation (Bash) Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/segment_anything_2_video.md This snippet outlines the bash commands to clone the label-studio-ml-backend repository, install its dependencies, and then install the specific requirements for the segment_anything_2_video example. ```bash git clone https://github.com/HumanSignal/label-studio-ml-backend.git cd label-studio-ml-backend pip install -e . cd label_studio_ml/examples/segment_anything_2_video pip install -r requirements.txt ``` -------------------------------- ### Initialize Project via CLI Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/setup.md Command to initialize a new Label Studio project using a local XML configuration file. ```bash label-studio my_new_project start --label-config config.xml ``` -------------------------------- ### Start Label Studio Instance Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/frontend.md After integrating a custom LSF build, start your Label Studio instance using this command. The instance will now use the new LSF build. ```bash label-studio start ``` -------------------------------- ### Start Label Studio Server for Audio Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/audio_regions/START.md Starts the Label Studio server using 'server.py'. It requires a configuration file ('config.json'), an XML configuration for audio regions ('config.xml'), a JSON file with tasks ('tasks.json'), and an output directory. ```bash python server.py -c config.json -l ../examples/audio_regions/config.xml -i ../examples/audio_regions/tasks.json -o output ``` -------------------------------- ### Start ML Backend from Source Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/nemo_asr.md Command to start the Label Studio ML backend after installing dependencies from source. It specifies the directory containing the ASR model. ```bash label-studio-ml start ./nemo_asr ``` -------------------------------- ### Initialize Label Studio ML Backend Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/tutorials/how_to_connect_Hugging_Face_with_Label_Studio_SDK.md Commands to install the ML backend SDK and initialize a new project structure for deploying custom models in production. ```bash pip install label-studio-ml label-studio-ml init my_ner_backend --script label_studio_ml/examples/simple_text_classifier.py ``` -------------------------------- ### Start Label Studio with Docker Compose Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/install_requirements.md Starts Label Studio using Docker Compose, typically with a PostgreSQL backend. Requires Docker Compose version 1.25.0 or higher. ```bash docker-compose up -d ``` -------------------------------- ### Start ML Backend without Docker Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/bert_classifier.md Starts the Label Studio ML backend from source without using Docker. This requires having the dependencies installed and specifying the directory containing your model. ```bash label-studio-ml start ./dir_with_your_model ``` -------------------------------- ### Start Label Studio Server for HTML Annotation Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/rich_text_html/START.md Starts the Label Studio server with configuration for named entity recognition on HTML markup. Requires a config file, task data, and an output path. ```bash python server.py -c config.json -l ../examples/html_document/config.xml -i ../examples/html_document/tasks.json -o output ``` -------------------------------- ### Run Label Studio with Docker (*nix) Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/install_requirements.md Starts Label Studio as a Docker container on Unix-like systems. It maps port 8080 and mounts a local directory for data storage. Requires Docker to be installed. ```bash docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest ``` -------------------------------- ### Install and Run Label Studio via Docker Source: https://github.com/humansignal/label-studio/blob/develop/README.md Commands to pull the official Label Studio image and run it as a container. This setup maps local storage to the container and exposes the service on port 8080. ```bash docker pull heartexlabs/label-studio:latest docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest ``` -------------------------------- ### Initialize Label Studio Project with ML Backend Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/dummy_model.md Initializes a new Label Studio project and connects it to a running ML backend instance. The --ml-backends flag specifies the URL of the backend service. ```bash label-studio start my_project --init --ml-backends http://localhost:9090 ``` -------------------------------- ### Start Dialogue Analysis Server Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/dialogue_analysis/START.md Starts the Label Studio server for analyzing chat dialogs, classifying them, and providing answers. Requires configuration and task files. ```bash python server.py -c config.json -l ../examples/chatbot_analysis/config.xml -i ../examples/chatbot_analysis/tasks.json -o output ``` -------------------------------- ### Install and Initialize ML Backend for GPT2 Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/gpt.md Installs the necessary Python packages and initializes a new ML backend using a GPT2 example script. This sets up the environment for integrating a language model with Label Studio. ```bash pip install -r label_studio_ml/examples/huggingface/requirements.txt label-studio-ml init my-ml-backend --from label_studio_ml/examples/huggingface/gpt.py label-studio-ml start my-ml-backend ``` -------------------------------- ### Install Dependencies and Run ML Backend without Docker Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/huggingface_ner.md Steps to set up and run the ML backend without Docker. It involves creating a virtual environment, installing requirements, and starting the backend service. ```bash python -m venv ml-backend source ml-backend/bin/activate pip install -r requirements.txt label-studio-ml start ./huggingface_ner ``` -------------------------------- ### Start Label Studio for Audio Classification Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/audio_classification/START.md Launches the Label Studio server with specified configuration files for audio classification, including task data and output. ```bash python server.py -c config.json -l ../examples/audio_classification/config.xml -i ../examples/audio_classification/tasks.json -o output ``` -------------------------------- ### Label Studio Result JSON Example Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/includes/tags/hypertext.md An example of the JSON output generated by Label Studio after a labeling task. This structure includes the start and end points of a selected region, offsets within those points, and associated labels. ```json { "value": { "start": "/div[1]/p[2]/text()[1]", "end": "/div[1]/p[4]/text()[3]", "startOffset": 2, "endOffset": 81, "hypertextlabels": ["Car"] } } ``` -------------------------------- ### Start HTML Document Annotation Server Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/html_document/START.md Starts the Label Studio server for Named Entity Recognition on HTML markup. It requires a configuration file, an XML config for labeling, a JSON file with tasks, and an output directory. ```bash python server.py -c config.json -l ../examples/html_document/config.xml -i ../examples/html_document/tasks.json -o output ``` -------------------------------- ### Usage Examples: Tailwind Classes with Design Tokens Source: https://github.com/humansignal/label-studio/blob/develop/web/tools/design-tokens-converter/README.md Illustrates how to apply the design tokens using Tailwind CSS utility classes. Examples cover colors, spacing, typography, and corner radius, demonstrating the integration with the `tailwind.config.js` setup. ```html
Styled with color tokens
Styled with spacing tokens

Styled with typography tokens

Styled with corner radius tokens
``` -------------------------------- ### Create Label Studio Project and Import Data Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/tutorials/how_to_create_a_Benchmark_and_Evaluate_your_models_with_Label_Studio.md Creates a new Label Studio project titled 'Phishing Benchmark Tutorial' within the specified workspace. It configures the project to be published and applies the previously defined labeling configuration. This action programmatically sets up the environment for data annotation. ```python # Creating project in the configured workspace project = ls.projects.create( title='Phishing Benchmark Tutorial', workspace=WORKSPACE_ID, is_published=True, label_config=label_config ) ``` -------------------------------- ### Start Hugging Face LLM Backend from Source Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/huggingface_llm.md This command starts the Hugging Face Large Language Model backend service using Python. It requires the project to be cloned and dependencies installed in a virtual environment. ```bash label-studio-ml start ./huggingface_llm ``` -------------------------------- ### Start Image Polygons Server Source: https://github.com/humansignal/label-studio/blob/develop/web/libs/editor/src/examples/image_polygons/START.md Launches the Label Studio server for Image Polygons with specified configuration and task files. ```bash python server.py -c config.json -l ../examples/image_polygons/config.xml -i ../examples/image_polygons/tasks.json -o output ``` -------------------------------- ### Building and running the ML backend from source Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/ml_tutorials/interactive_substring_matching.md Instructions for building the Docker image from source and running the backend in a virtual environment without Docker. ```bash docker-compose build ``` ```bash python -m venv ml-backend source ml-backend/bin/activate pip install -r requirements.txt label-studio-ml start ./interactive_substring_matching ``` -------------------------------- ### Initialize Label Studio Frontend with Plain HTML and JS Source: https://github.com/humansignal/label-studio/blob/develop/docs/source/guide/frontend.md Use this snippet for static HTML pages or when not using a bundler. It includes the necessary CSS and JS files directly. ```html
```