label-studio-ml-backend | Label Studio 's Machine Learning backend | Data Labeling library

 by   heartexlabs Python Version: Current License: Apache-2.0

kandi X-RAY | label-studio-ml-backend Summary

kandi X-RAY | label-studio-ml-backend Summary

label-studio-ml-backend is a Python library typically used in Artificial Intelligence, Data Labeling, Docker applications. label-studio-ml-backend has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However label-studio-ml-backend has 10 bugs. You can install using 'pip install label-studio-ml-backend' or download it from GitHub, PyPI.

The Label Studio ML backend is an SDK that lets you wrap your machine learning code and turn it into a web server. You can then connect that server to a Label Studio instance to perform 2 tasks:. If you just need to load static pre-annotated data into Label Studio, running an ML backend might be overkill for you. Instead, you can import preannotated data.

            kandi-support Support

              label-studio-ml-backend has a low active ecosystem.
              It has 235 star(s) with 112 fork(s). There are 9 watchers for this library.
              It had no major release in the last 6 months.
              There are 40 open issues and 94 have been closed. On average issues are closed in 148 days. There are 32 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of label-studio-ml-backend is current.

            kandi-Quality Quality

              label-studio-ml-backend has 10 bugs (0 blocker, 0 critical, 9 major, 1 minor) and 36 code smells.

            kandi-Security Security

              label-studio-ml-backend has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              label-studio-ml-backend code analysis shows 0 unresolved vulnerabilities.
              There are 16 security hotspots that need review.

            kandi-License License

              label-studio-ml-backend 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.

            kandi-Reuse Reuse

              label-studio-ml-backend releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 3867 lines of code, 214 functions and 38 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed label-studio-ml-backend and discovered the below as its top functions. This is intended to give you an instant insight into label-studio-ml-backend implemented functionality, and help decide if they suit your requirements.
            • Fit a pre - trained model
            • Dump the corpus to a file
            • Get padding function
            • Returns a dictionary of the parameters
            • Fit the model to the given completions
            • Reset the pretrained model
            • Prepare data for training
            • Pads a sequence of sequences to maxlen
            • Predict a single task
            • Deploy to GCP
            • Decorator to handle exceptions
            • Runs the ASR
            • Run a single job
            • Create a prediction example for the given tasks
            • Fit the model with the given completions
            • Predict from the given tasks
            • Create model directory
            • Predict results
            • Predict the prediction
            • Predict a list of tasks
            • Create a flair model for the given completions
            • Train the model
            • Predict given tasks
            • Run inference on the given image
            • Get command line arguments
            • Run prediction on the given tasks
            Get all kandi verified functions for this library.

            label-studio-ml-backend Key Features

            No Key Features are available at this moment for label-studio-ml-backend.

            label-studio-ml-backend Examples and Code Snippets

            No Code Snippets are available at this moment for label-studio-ml-backend.

            Community Discussions


            How can I do this split process in Python?
            Asked 2021-Dec-30 at 14:06

            I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

            What I've done so far is make this representation with the same enumerator class.

            A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?



            Answered 2021-Dec-30 at 13:57

            Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:



            Replacing a character with a space and dividing the string into two words in R
            Asked 2020-Nov-18 at 07:32

            I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

            In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

            i have this:



            Answered 2020-Nov-16 at 07:24


            Azure ML FileDataset registers, but cannot be accessed for Data Labeling project
            Asked 2020-Oct-28 at 20:31

            Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

            Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

            Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.



            Answered 2020-Oct-27 at 22:39

            Is the data behind virtual network by any chance?


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install label-studio-ml-backend

            Follow this example tutorial to run an ML backend with a simple text classifier:.
            Clone the repo git clone
            Setup environment It is highly recommended to use venv, virtualenv or conda python environments. You can use the same environment as Label Studio does. Read more about creating virtual environments via venv. cd label-studio-ml-backend # Install label-studio-ml and its dependencies pip install -U -e . # Install example dependencies pip install -r label_studio_ml/examples/requirements.txt
            Initialize an ML backend based on an example script: label-studio-ml init my_ml_backend --script label_studio_ml/examples/simple_text_classifier/ This ML backend is an example provided by Label Studio. See how to create your own ML backend.
            Start ML backend server label-studio-ml start my_ml_backend
            Start Label Studio and connect it to the running ML backend on the project settings page.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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