%global _empty_manifest_terminate_build 0 Name: python-label-studio Version: 1.7.3 Release: 1 Summary: Label Studio annotation tool License: Apache Software License URL: https://github.com/heartexlabs/label-studio Source0: https://mirrors.nju.edu.cn/pypi/web/packages/33/2b/ff76b68ea7bc4abd6557966e896e817a6d14ba0f5f31ddc8006335bf3172/label-studio-1.7.3.tar.gz BuildArch: noarch Requires: python3-wheel Requires: python3-appdirs Requires: python3-attr Requires: python3-attrs Requires: python3-pyyaml Requires: python3-azure-storage-blob Requires: python3-boto Requires: python3-boto3 Requires: python3-botocore Requires: python3-bleach Requires: python3-google-api-core Requires: python3-google-auth Requires: python3-google-cloud-appengine-logging Requires: python3-google-cloud-audit-log Requires: python3-google-cloud-core Requires: python3-google-cloud-storage Requires: python3-google-cloud-logging Requires: python3-google-resumable-media Requires: python3-googleapis-common-protos Requires: python3-grpc-google-iam-v1 Requires: python3-Django Requires: python3-django-storages Requires: python3-django-annoying Requires: python3-django-debug-toolbar Requires: python3-django-filter Requires: python3-django-model-utils Requires: python3-django-rq Requires: python3-django-cors-headers Requires: python3-django-extensions Requires: python3-django-rest-swagger Requires: python3-django-user-agents Requires: python3-django-ranged-fileresponse Requires: python3-drf-dynamic-fields Requires: python3-djangorestframework Requires: python3-drf-flex-fields Requires: python3-drf-yasg Requires: python3-drf-generators Requires: python3-htmlmin Requires: python3-jsonschema Requires: python3-lockfile Requires: python3-lxml Requires: python3-defusedxml Requires: python3-numpy Requires: python3-ordered-set Requires: python3-pandas Requires: python3-protobuf Requires: python3-psycopg2-binary Requires: python3-pydantic Requires: python3-dateutil Requires: python3-pytz Requires: python3-requests Requires: python3-rq Requires: python3-rules Requires: python3-ujson Requires: python3-xmljson Requires: python3-colorama Requires: python3-boxing Requires: python3-redis Requires: python3-sentry-sdk Requires: python3-launchdarkly-server-sdk Requires: python3-json-logger Requires: python3-label-studio-converter Requires: python3-mysqlclient %description ![GitHub](https://img.shields.io/github/license/heartexlabs/label-studio?logo=heartex) ![label-studio:build](https://github.com/heartexlabs/label-studio/workflows/label-studio:build/badge.svg) ![GitHub release](https://img.shields.io/github/v/release/heartexlabs/label-studio?include_prereleases) [Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide/) • [Twitter](https://twitter.com/labelstudiohq) • [Join Slack Community ](https://slack.labelstudio.heartex.com/?source=github-1) ## What is Label Studio? Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. - [Try out Label Studio](#try-out-label-studio) - [What you get from Label Studio](#what-you-get-from-label-studio) - [Included templates for labeling data in Label Studio](#included-templates-for-labeling-data-in-label-studio) - [Set up machine learning models with Label Studio](#set-up-machine-learning-models-with-Label-Studio) - [Integrate Label Studio with your existing tools](#integrate-label-studio-with-your-existing-tools) ![Gif of Label Studio annotating different types of data](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/annotation_examples.gif) Have a custom dataset? You can customize Label Studio to fit your needs. Read an [introductory blog post](https://towardsdatascience.com/introducing-label-studio-a-swiss-army-knife-of-data-labeling-140c1be92881) to learn more. ## Try out Label Studio Install Label Studio locally, or deploy it in a cloud instance. [Or, sign up for a free trial of our Enterprise edition.](https://heartex.com/free-trial). - [Install locally with Docker](#install-locally-with-docker) - [Run with Docker Compose (Label Studio + Nginx + PostgreSQL)](#run-with-docker-compose) - [Install locally with pip](#install-locally-with-pip) - [Install locally with Anaconda](#install-locally-with-anaconda) - [Install for local development](#install-for-local-development) - [Deploy in a cloud instance](#deploy-in-a-cloud-instance) ### Install locally with Docker Official Label Studio docker image is [here](https://hub.docker.com/r/heartexlabs/label-studio) and it can be downloaded with `docker pull`. Run Label Studio in a Docker container and access it at `http://localhost:8080`. ```bash docker pull heartexlabs/label-studio:latest docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest ``` You can find all the generated assets, including SQLite3 database storage `label_studio.sqlite3` and uploaded files, in the `./mydata` directory. #### Override default Docker install You can override the default launch command by appending the new arguments: ```bash docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio --log-level DEBUG ``` #### Build a local image with Docker If you want to build a local image, run: ```bash docker build -t heartexlabs/label-studio:latest . ``` ### Run with Docker Compose Docker Compose script provides production-ready stack consisting of the following components: - Label Studio - [Nginx](https://www.nginx.com/) - proxy web server used to load various static data, including uploaded audio, images, etc. - [PostgreSQL](https://www.postgresql.org/) - production-ready database that replaces less performant SQLite3. To start using the app from `http://localhost` run this command: ```bash docker-compose up ``` ### Install locally with pip ```bash # Requires Python >=3.7 <=3.9 pip install label-studio # Start the server at http://localhost:8080 label-studio ``` ### Install locally with Anaconda ```bash conda create --name label-studio conda activate label-studio pip install label-studio ``` ### Install for local development You can run the latest Label Studio version locally without installing the package with pip. ```bash # Install all package dependencies pip install -e . # Run database migrations python label_studio/manage.py migrate python label_studio/manage.py collectstatic # Start the server in development mode at http://localhost:8080 python label_studio/manage.py runserver ``` ### Deploy in a cloud instance You can deploy Label Studio with one click in Heroku, Microsoft Azure, or Google Cloud Platform: [](https://heroku.com/deploy?template=https://github.com/heartexlabs/label-studio/tree/heroku-persistent-pg) [](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fheartexlabs%2Flabel-studio%2Fmaster%2Fazuredeploy.json) [](https://deploy.cloud.run) #### Apply frontend changes The frontend part of Label Studio app lies in the `frontend/` folder and written in React JSX. In case you've made some changes there, the following commands should be run before building / starting the instance: ``` cd label_studio/frontend/ npm ci npx webpack cd ../.. python label_studio/manage.py collectstatic --no-input ``` ### Troubleshoot installation If you see any errors during installation, try to rerun the installation ```bash pip install --ignore-installed label-studio ``` #### Install dependencies on Windows To run Label Studio on Windows, download and install the following wheel packages from [Gohlke builds](https://www.lfd.uci.edu/~gohlke/pythonlibs) to ensure you're using the correct version of Python: - [lxml](https://www.lfd.uci.edu/~gohlke/pythonlibs/#lxml) ```bash # Upgrade pip pip install -U pip # If you're running Win64 with Python 3.8, install the packages downloaded from Gohlke: pip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl # Install label studio pip install label-studio ``` #### Run test suite ```bash pip install -r deploy/requirements-test.txt cd label_studio # postgres (assumes default postgres user,db,pass) DJANGO_DB=default DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto # sqlite3 DJANGO_DB=sqlite DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto ``` ## What you get from Label Studio ![Screenshot of Label Studio data manager grid view with images](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/labelstudio-ui.gif) - **Multi-user labeling** sign up and login, when you create an annotation it's tied to your account. - **Multiple projects** to work on all your datasets in one instance. - **Streamlined design** helps you focus on your task, not how to use the software. - **Configurable label formats** let you customize the visual interface to meet your specific labeling needs. - **Support for multiple data types** including images, audio, text, HTML, time-series, and video. - **Import from files or from cloud storage** in Amazon AWS S3, Google Cloud Storage, or JSON, CSV, TSV, RAR, and ZIP archives. - **Integration with machine learning models** so that you can visualize and compare predictions from different models and perform pre-labeling. - **Embed it in your data pipeline** REST API makes it easy to make it a part of your pipeline ## Included templates for labeling data in Label Studio Label Studio includes a variety of templates to help you label your data, or you can create your own using specifically designed configuration language. The most common templates and use cases for labeling include the following cases: ## Set up machine learning models with Label Studio Connect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps: 1. Start your own machine learning backend server. See [more detailed instructions](https://github.com/heartexlabs/label-studio-ml-backend). 2. Connect Label Studio to the server on the model page found in project settings. This lets you: - **Pre-label** your data using model predictions. - Do **online learning** and retrain your model while new annotations are being created. - Do **active learning** by labeling only the most complex examples in your data. ## Integrate Label Studio with your existing tools You can use Label Studio as an independent part of your machine learning workflow or integrate the frontend or backend into your existing tools. * Use the [Label Studio Frontend](https://github.com/heartexlabs/label-studio-frontend) as a separate React library. See more in the [Frontend Library documentation](https://labelstud.io/guide/frontend.html). ## Ecosystem | Project | Description | |-|-| | label-studio | Server, distributed as a pip package | | [label-studio-frontend](https://github.com/heartexlabs/label-studio-frontend) | React and JavaScript frontend and can run standalone in a web browser or be embedded into your application. | | [data-manager](https://github.com/heartexlabs/dm2) | React and JavaScript frontend for managing data. Includes the Label Studio Frontend. Relies on the label-studio server or a custom backend with the expected API methods. | | [label-studio-converter](https://github.com/heartexlabs/label-studio-converter) | Encode labels in the format of your favorite machine learning library | | [label-studio-transformers](https://github.com/heartexlabs/label-studio-transformers) | Transformers library connected and configured for use with Label Studio | ## Roadmap Want to use **The Coolest Feature X** but Label Studio doesn't support it? Check out [our public roadmap](roadmap.md)! ## Citation ```tex @misc{Label Studio, title={{Label Studio}: Data labeling software}, url={https://github.com/heartexlabs/label-studio}, note={Open source software available from https://github.com/heartexlabs/label-studio}, author={ Maxim Tkachenko and Mikhail Malyuk and Andrey Holmanyuk and Nikolai Liubimov}, year={2020-2022}, } ``` ## License This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020-2022 %package -n python3-label-studio Summary: Label Studio annotation tool Provides: python-label-studio BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-label-studio ![GitHub](https://img.shields.io/github/license/heartexlabs/label-studio?logo=heartex) ![label-studio:build](https://github.com/heartexlabs/label-studio/workflows/label-studio:build/badge.svg) ![GitHub release](https://img.shields.io/github/v/release/heartexlabs/label-studio?include_prereleases) [Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide/) • [Twitter](https://twitter.com/labelstudiohq) • [Join Slack Community ](https://slack.labelstudio.heartex.com/?source=github-1) ## What is Label Studio? Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. - [Try out Label Studio](#try-out-label-studio) - [What you get from Label Studio](#what-you-get-from-label-studio) - [Included templates for labeling data in Label Studio](#included-templates-for-labeling-data-in-label-studio) - [Set up machine learning models with Label Studio](#set-up-machine-learning-models-with-Label-Studio) - [Integrate Label Studio with your existing tools](#integrate-label-studio-with-your-existing-tools) ![Gif of Label Studio annotating different types of data](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/annotation_examples.gif) Have a custom dataset? You can customize Label Studio to fit your needs. Read an [introductory blog post](https://towardsdatascience.com/introducing-label-studio-a-swiss-army-knife-of-data-labeling-140c1be92881) to learn more. ## Try out Label Studio Install Label Studio locally, or deploy it in a cloud instance. [Or, sign up for a free trial of our Enterprise edition.](https://heartex.com/free-trial). - [Install locally with Docker](#install-locally-with-docker) - [Run with Docker Compose (Label Studio + Nginx + PostgreSQL)](#run-with-docker-compose) - [Install locally with pip](#install-locally-with-pip) - [Install locally with Anaconda](#install-locally-with-anaconda) - [Install for local development](#install-for-local-development) - [Deploy in a cloud instance](#deploy-in-a-cloud-instance) ### Install locally with Docker Official Label Studio docker image is [here](https://hub.docker.com/r/heartexlabs/label-studio) and it can be downloaded with `docker pull`. Run Label Studio in a Docker container and access it at `http://localhost:8080`. ```bash docker pull heartexlabs/label-studio:latest docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest ``` You can find all the generated assets, including SQLite3 database storage `label_studio.sqlite3` and uploaded files, in the `./mydata` directory. #### Override default Docker install You can override the default launch command by appending the new arguments: ```bash docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio --log-level DEBUG ``` #### Build a local image with Docker If you want to build a local image, run: ```bash docker build -t heartexlabs/label-studio:latest . ``` ### Run with Docker Compose Docker Compose script provides production-ready stack consisting of the following components: - Label Studio - [Nginx](https://www.nginx.com/) - proxy web server used to load various static data, including uploaded audio, images, etc. - [PostgreSQL](https://www.postgresql.org/) - production-ready database that replaces less performant SQLite3. To start using the app from `http://localhost` run this command: ```bash docker-compose up ``` ### Install locally with pip ```bash # Requires Python >=3.7 <=3.9 pip install label-studio # Start the server at http://localhost:8080 label-studio ``` ### Install locally with Anaconda ```bash conda create --name label-studio conda activate label-studio pip install label-studio ``` ### Install for local development You can run the latest Label Studio version locally without installing the package with pip. ```bash # Install all package dependencies pip install -e . # Run database migrations python label_studio/manage.py migrate python label_studio/manage.py collectstatic # Start the server in development mode at http://localhost:8080 python label_studio/manage.py runserver ``` ### Deploy in a cloud instance You can deploy Label Studio with one click in Heroku, Microsoft Azure, or Google Cloud Platform: [](https://heroku.com/deploy?template=https://github.com/heartexlabs/label-studio/tree/heroku-persistent-pg) [](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fheartexlabs%2Flabel-studio%2Fmaster%2Fazuredeploy.json) [](https://deploy.cloud.run) #### Apply frontend changes The frontend part of Label Studio app lies in the `frontend/` folder and written in React JSX. In case you've made some changes there, the following commands should be run before building / starting the instance: ``` cd label_studio/frontend/ npm ci npx webpack cd ../.. python label_studio/manage.py collectstatic --no-input ``` ### Troubleshoot installation If you see any errors during installation, try to rerun the installation ```bash pip install --ignore-installed label-studio ``` #### Install dependencies on Windows To run Label Studio on Windows, download and install the following wheel packages from [Gohlke builds](https://www.lfd.uci.edu/~gohlke/pythonlibs) to ensure you're using the correct version of Python: - [lxml](https://www.lfd.uci.edu/~gohlke/pythonlibs/#lxml) ```bash # Upgrade pip pip install -U pip # If you're running Win64 with Python 3.8, install the packages downloaded from Gohlke: pip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl # Install label studio pip install label-studio ``` #### Run test suite ```bash pip install -r deploy/requirements-test.txt cd label_studio # postgres (assumes default postgres user,db,pass) DJANGO_DB=default DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto # sqlite3 DJANGO_DB=sqlite DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto ``` ## What you get from Label Studio ![Screenshot of Label Studio data manager grid view with images](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/labelstudio-ui.gif) - **Multi-user labeling** sign up and login, when you create an annotation it's tied to your account. - **Multiple projects** to work on all your datasets in one instance. - **Streamlined design** helps you focus on your task, not how to use the software. - **Configurable label formats** let you customize the visual interface to meet your specific labeling needs. - **Support for multiple data types** including images, audio, text, HTML, time-series, and video. - **Import from files or from cloud storage** in Amazon AWS S3, Google Cloud Storage, or JSON, CSV, TSV, RAR, and ZIP archives. - **Integration with machine learning models** so that you can visualize and compare predictions from different models and perform pre-labeling. - **Embed it in your data pipeline** REST API makes it easy to make it a part of your pipeline ## Included templates for labeling data in Label Studio Label Studio includes a variety of templates to help you label your data, or you can create your own using specifically designed configuration language. The most common templates and use cases for labeling include the following cases: ## Set up machine learning models with Label Studio Connect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps: 1. Start your own machine learning backend server. See [more detailed instructions](https://github.com/heartexlabs/label-studio-ml-backend). 2. Connect Label Studio to the server on the model page found in project settings. This lets you: - **Pre-label** your data using model predictions. - Do **online learning** and retrain your model while new annotations are being created. - Do **active learning** by labeling only the most complex examples in your data. ## Integrate Label Studio with your existing tools You can use Label Studio as an independent part of your machine learning workflow or integrate the frontend or backend into your existing tools. * Use the [Label Studio Frontend](https://github.com/heartexlabs/label-studio-frontend) as a separate React library. See more in the [Frontend Library documentation](https://labelstud.io/guide/frontend.html). ## Ecosystem | Project | Description | |-|-| | label-studio | Server, distributed as a pip package | | [label-studio-frontend](https://github.com/heartexlabs/label-studio-frontend) | React and JavaScript frontend and can run standalone in a web browser or be embedded into your application. | | [data-manager](https://github.com/heartexlabs/dm2) | React and JavaScript frontend for managing data. Includes the Label Studio Frontend. Relies on the label-studio server or a custom backend with the expected API methods. | | [label-studio-converter](https://github.com/heartexlabs/label-studio-converter) | Encode labels in the format of your favorite machine learning library | | [label-studio-transformers](https://github.com/heartexlabs/label-studio-transformers) | Transformers library connected and configured for use with Label Studio | ## Roadmap Want to use **The Coolest Feature X** but Label Studio doesn't support it? Check out [our public roadmap](roadmap.md)! ## Citation ```tex @misc{Label Studio, title={{Label Studio}: Data labeling software}, url={https://github.com/heartexlabs/label-studio}, note={Open source software available from https://github.com/heartexlabs/label-studio}, author={ Maxim Tkachenko and Mikhail Malyuk and Andrey Holmanyuk and Nikolai Liubimov}, year={2020-2022}, } ``` ## License This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020-2022 %package help Summary: Development documents and examples for label-studio Provides: python3-label-studio-doc %description help ![GitHub](https://img.shields.io/github/license/heartexlabs/label-studio?logo=heartex) ![label-studio:build](https://github.com/heartexlabs/label-studio/workflows/label-studio:build/badge.svg) ![GitHub release](https://img.shields.io/github/v/release/heartexlabs/label-studio?include_prereleases) [Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide/) • [Twitter](https://twitter.com/labelstudiohq) • [Join Slack Community ](https://slack.labelstudio.heartex.com/?source=github-1) ## What is Label Studio? Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. - [Try out Label Studio](#try-out-label-studio) - [What you get from Label Studio](#what-you-get-from-label-studio) - [Included templates for labeling data in Label Studio](#included-templates-for-labeling-data-in-label-studio) - [Set up machine learning models with Label Studio](#set-up-machine-learning-models-with-Label-Studio) - [Integrate Label Studio with your existing tools](#integrate-label-studio-with-your-existing-tools) ![Gif of Label Studio annotating different types of data](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/annotation_examples.gif) Have a custom dataset? You can customize Label Studio to fit your needs. Read an [introductory blog post](https://towardsdatascience.com/introducing-label-studio-a-swiss-army-knife-of-data-labeling-140c1be92881) to learn more. ## Try out Label Studio Install Label Studio locally, or deploy it in a cloud instance. [Or, sign up for a free trial of our Enterprise edition.](https://heartex.com/free-trial). - [Install locally with Docker](#install-locally-with-docker) - [Run with Docker Compose (Label Studio + Nginx + PostgreSQL)](#run-with-docker-compose) - [Install locally with pip](#install-locally-with-pip) - [Install locally with Anaconda](#install-locally-with-anaconda) - [Install for local development](#install-for-local-development) - [Deploy in a cloud instance](#deploy-in-a-cloud-instance) ### Install locally with Docker Official Label Studio docker image is [here](https://hub.docker.com/r/heartexlabs/label-studio) and it can be downloaded with `docker pull`. Run Label Studio in a Docker container and access it at `http://localhost:8080`. ```bash docker pull heartexlabs/label-studio:latest docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest ``` You can find all the generated assets, including SQLite3 database storage `label_studio.sqlite3` and uploaded files, in the `./mydata` directory. #### Override default Docker install You can override the default launch command by appending the new arguments: ```bash docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio --log-level DEBUG ``` #### Build a local image with Docker If you want to build a local image, run: ```bash docker build -t heartexlabs/label-studio:latest . ``` ### Run with Docker Compose Docker Compose script provides production-ready stack consisting of the following components: - Label Studio - [Nginx](https://www.nginx.com/) - proxy web server used to load various static data, including uploaded audio, images, etc. - [PostgreSQL](https://www.postgresql.org/) - production-ready database that replaces less performant SQLite3. To start using the app from `http://localhost` run this command: ```bash docker-compose up ``` ### Install locally with pip ```bash # Requires Python >=3.7 <=3.9 pip install label-studio # Start the server at http://localhost:8080 label-studio ``` ### Install locally with Anaconda ```bash conda create --name label-studio conda activate label-studio pip install label-studio ``` ### Install for local development You can run the latest Label Studio version locally without installing the package with pip. ```bash # Install all package dependencies pip install -e . # Run database migrations python label_studio/manage.py migrate python label_studio/manage.py collectstatic # Start the server in development mode at http://localhost:8080 python label_studio/manage.py runserver ``` ### Deploy in a cloud instance You can deploy Label Studio with one click in Heroku, Microsoft Azure, or Google Cloud Platform: [](https://heroku.com/deploy?template=https://github.com/heartexlabs/label-studio/tree/heroku-persistent-pg) [](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fheartexlabs%2Flabel-studio%2Fmaster%2Fazuredeploy.json) [](https://deploy.cloud.run) #### Apply frontend changes The frontend part of Label Studio app lies in the `frontend/` folder and written in React JSX. In case you've made some changes there, the following commands should be run before building / starting the instance: ``` cd label_studio/frontend/ npm ci npx webpack cd ../.. python label_studio/manage.py collectstatic --no-input ``` ### Troubleshoot installation If you see any errors during installation, try to rerun the installation ```bash pip install --ignore-installed label-studio ``` #### Install dependencies on Windows To run Label Studio on Windows, download and install the following wheel packages from [Gohlke builds](https://www.lfd.uci.edu/~gohlke/pythonlibs) to ensure you're using the correct version of Python: - [lxml](https://www.lfd.uci.edu/~gohlke/pythonlibs/#lxml) ```bash # Upgrade pip pip install -U pip # If you're running Win64 with Python 3.8, install the packages downloaded from Gohlke: pip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl # Install label studio pip install label-studio ``` #### Run test suite ```bash pip install -r deploy/requirements-test.txt cd label_studio # postgres (assumes default postgres user,db,pass) DJANGO_DB=default DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto # sqlite3 DJANGO_DB=sqlite DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto ``` ## What you get from Label Studio ![Screenshot of Label Studio data manager grid view with images](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/labelstudio-ui.gif) - **Multi-user labeling** sign up and login, when you create an annotation it's tied to your account. - **Multiple projects** to work on all your datasets in one instance. - **Streamlined design** helps you focus on your task, not how to use the software. - **Configurable label formats** let you customize the visual interface to meet your specific labeling needs. - **Support for multiple data types** including images, audio, text, HTML, time-series, and video. - **Import from files or from cloud storage** in Amazon AWS S3, Google Cloud Storage, or JSON, CSV, TSV, RAR, and ZIP archives. - **Integration with machine learning models** so that you can visualize and compare predictions from different models and perform pre-labeling. - **Embed it in your data pipeline** REST API makes it easy to make it a part of your pipeline ## Included templates for labeling data in Label Studio Label Studio includes a variety of templates to help you label your data, or you can create your own using specifically designed configuration language. The most common templates and use cases for labeling include the following cases: ## Set up machine learning models with Label Studio Connect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps: 1. Start your own machine learning backend server. See [more detailed instructions](https://github.com/heartexlabs/label-studio-ml-backend). 2. Connect Label Studio to the server on the model page found in project settings. This lets you: - **Pre-label** your data using model predictions. - Do **online learning** and retrain your model while new annotations are being created. - Do **active learning** by labeling only the most complex examples in your data. ## Integrate Label Studio with your existing tools You can use Label Studio as an independent part of your machine learning workflow or integrate the frontend or backend into your existing tools. * Use the [Label Studio Frontend](https://github.com/heartexlabs/label-studio-frontend) as a separate React library. See more in the [Frontend Library documentation](https://labelstud.io/guide/frontend.html). ## Ecosystem | Project | Description | |-|-| | label-studio | Server, distributed as a pip package | | [label-studio-frontend](https://github.com/heartexlabs/label-studio-frontend) | React and JavaScript frontend and can run standalone in a web browser or be embedded into your application. | | [data-manager](https://github.com/heartexlabs/dm2) | React and JavaScript frontend for managing data. Includes the Label Studio Frontend. Relies on the label-studio server or a custom backend with the expected API methods. | | [label-studio-converter](https://github.com/heartexlabs/label-studio-converter) | Encode labels in the format of your favorite machine learning library | | [label-studio-transformers](https://github.com/heartexlabs/label-studio-transformers) | Transformers library connected and configured for use with Label Studio | ## Roadmap Want to use **The Coolest Feature X** but Label Studio doesn't support it? Check out [our public roadmap](roadmap.md)! ## Citation ```tex @misc{Label Studio, title={{Label Studio}: Data labeling software}, url={https://github.com/heartexlabs/label-studio}, note={Open source software available from https://github.com/heartexlabs/label-studio}, author={ Maxim Tkachenko and Mikhail Malyuk and Andrey Holmanyuk and Nikolai Liubimov}, year={2020-2022}, } ``` ## License This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020-2022 %prep %autosetup -n label-studio-1.7.3 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-label-studio -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 25 2023 Python_Bot - 1.7.3-1 - Package Spec generated