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create_annotation_spec_set.py
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create_annotation_spec_set_test.py
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create_instruction.py
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create_instruction_test.py
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export_data.py
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import_data.py
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import_data_test.py
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label_image.py
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label_image_test.py
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label_text.py
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label_text_test.py
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label_video.py
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label_video_test.py
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manage_dataset.py
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manage_dataset_test.py
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requirements.txt
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README.rst

Google Cloud Data Labeling Service Python Samples

https://gstatic.com/cloudssh/images/open-btn.png

This directory contains samples for Google Cloud Data Labeling Service. Google Cloud Data Labeling Service allows developers to request having human labelers label a collection of data that you plan to use to train a custom machine learning model.

Setup

Authentication

This sample requires you to have authentication setup. Refer to the Authentication Getting Started Guide for instructions on setting up credentials for applications.

Install Dependencies

  1. Clone python-docs-samples and change directory to the sample directory you want to use.

    $ git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git

  2. Install pip and virtualenv if you do not already have them. You may want to refer to the Python Development Environment Setup Guide for Google Cloud Platform for instructions.

  3. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.

    $ virtualenv env
$ source env/bin/activate

  4. Install the dependencies needed to run the samples.

    $ pip install -r requirements.txt

The client library

This sample uses the Google Cloud Client Library for Python. You can read the documentation for more details on API usage and use GitHub to browse the source and report issues.