labelit | Data labelling platform for small teams | Data Labeling library
kandi X-RAY | labelit Summary
kandi X-RAY | labelit Summary
labelit is a Python library typically used in Artificial Intelligence, Data Labeling applications. labelit has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Labelit is a data labelling platform based on open source tool Label Studio. We want to help small teams (having 3 to 5 annotators) who use Label Studio to manage multiple projects.
Labelit is a data labelling platform based on open source tool Label Studio. We want to help small teams (having 3 to 5 annotators) who use Label Studio to manage multiple projects.
Support
Quality
Security
License
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Support
labelit has a low active ecosystem.
It has 2 star(s) with 2 fork(s). There are 4 watchers for this library.
It had no major release in the last 12 months.
There are 5 open issues and 9 have been closed. On average issues are closed in 26 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of labelit is 0.1-beta
Quality
labelit has 0 bugs and 0 code smells.
Security
labelit has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
labelit code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
labelit is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
labelit releases are available to install and integrate.
Build file is available. You can build the component from source.
Installation instructions are available. Examples and code snippets are not available.
It has 1519 lines of code, 43 functions and 45 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed labelit and discovered the below as its top functions. This is intended to give you an instant insight into labelit implemented functionality, and help decide if they suit your requirements.
- Manage server servers .
- Dispatch to a specific project .
- View for labeling a label
- Add or remove annotators .
- Export all projects .
- Delete project data .
- View for labelit tasks .
- Edit a project .
- Stop running project servers
- Delete a project .
Get all kandi verified functions for this library.
labelit Key Features
No Key Features are available at this moment for labelit.
labelit Examples and Code Snippets
No Code Snippets are available at this moment for labelit.
Community Discussions
Trending Discussions on labelit
QUESTION
Error in v-on handler (Promise/async): "TypeError: axios__WEBPACK_IMPORTED_MODULE_14___default.a.todo is not a function"
Asked 2020-Jan-12 at 01:04
I am creating a simple todo list. Before creating the createTodo() in the methods section, I was able to successfully post data (text) to PostMan. This was the server portion of the application. Now on to the client portion. Now, I am unable to post to the mongo database. Any help on this would be greatly appreciated. Thank you. The code is as follows:
...ANSWER
Answered 2020-Jan-12 at 01:04The problem is this line:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install labelit
Download latest release from releases page using Source code option and decompress the downloaded file.
Create a conda environment (recommended) : conda create -n label-studio python=3.7
Switch to conda env (recommended) : conda activate label-studio
Install requirements for Labelit : pip install -r requirements.txt
Enter Django app directory : cd labelit
Set secret key for Django as Environment variable (You can generate secret key using https://gist.github.com/ndarville/3452907 or https://djecrety.ir): export DJANGO_SECRET_KEY="<generated_key>"
Perform database migration (By default, it uses local SQLite database. If you want to connect to another database, check Django documentation) : python manage.py migrate
Create admin user : python manage.py createsuperuser
Run the server using gunicorn : gunicorn --worker-class=gevent --worker-connections=500 --workers=1 -b '0.0.0.0:8000' labelit.wsgi
Clone the repo : git clone git@github.com:verloop/labelit.git; cd labelit
Create a conda environment (recommended) : conda create -n label-studio python=3.7
Switch to conda env (recommended) : conda activate label-studio
Install requirements for Labelit : pip install -r requirements.txt
Enter Django app directory : cd labelit
Set secret key for Django as Environment variable (You can generate secret key using https://gist.github.com/ndarville/3452907 or https://djecrety.ir): export DJANGO_SECRET_KEY="<generated_key>"
Perform database migration (By default, it uses local SQLite database. If you want to connect to another database, check Django documentation) : python manage.py migrate
Create admin user : python manage.py createsuperuser
Run the development server : python manage.py runserver --noreload
Create a conda environment (recommended) : conda create -n label-studio python=3.7
Switch to conda env (recommended) : conda activate label-studio
Install requirements for Labelit : pip install -r requirements.txt
Enter Django app directory : cd labelit
Set secret key for Django as Environment variable (You can generate secret key using https://gist.github.com/ndarville/3452907 or https://djecrety.ir): export DJANGO_SECRET_KEY="<generated_key>"
Perform database migration (By default, it uses local SQLite database. If you want to connect to another database, check Django documentation) : python manage.py migrate
Create admin user : python manage.py createsuperuser
Run the server using gunicorn : gunicorn --worker-class=gevent --worker-connections=500 --workers=1 -b '0.0.0.0:8000' labelit.wsgi
Clone the repo : git clone git@github.com:verloop/labelit.git; cd labelit
Create a conda environment (recommended) : conda create -n label-studio python=3.7
Switch to conda env (recommended) : conda activate label-studio
Install requirements for Labelit : pip install -r requirements.txt
Enter Django app directory : cd labelit
Set secret key for Django as Environment variable (You can generate secret key using https://gist.github.com/ndarville/3452907 or https://djecrety.ir): export DJANGO_SECRET_KEY="<generated_key>"
Perform database migration (By default, it uses local SQLite database. If you want to connect to another database, check Django documentation) : python manage.py migrate
Create admin user : python manage.py createsuperuser
Run the development server : python manage.py runserver --noreload
Support
Labelit now supports datasets that are stored remotely. We have right now added plugins for the following storage :. Since this is optional, Labelit by default doesn't install the packages needed for accessing data from these storages. Install package for the storage you need: For AWS S3 support : pip install boto3 For Google storage support : pip install google-cloud-storage. Make sure the instance/machine is configured with access to the remote storage and check Django settings (./labelit/labelit/settings.py) for few additional settings. And that's it! Labelit automatically detects the storage type when you give it while creating project, just make sure you use the right prefix (eg. s3:// for AWS s3 storage). Currently, Labelit only supports downloading dataset from remote storage. This is performed only one time after project is created. So, in case the data in remote storage is modified later, it won't reflect here. We will be adding support for remote upload soon to automatically upload labelled data to remote storage.
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