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IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/Makefile b/Makefile deleted file mode 100644 index 8ee19e00b..000000000 --- a/Makefile +++ /dev/null @@ -1,78 +0,0 @@ - -build-image: - docker build --quiet -t local/labelbox-python:test . - -test-local: build-image - - @# if PATH_TO_TEST we assume you know what you are doing - @if [ -z ${PATH_TO_TEST} ]; then \ - ./scripts/ensure_local_setup.sh; \ - fi - - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="local" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e LABELBOX_TEST_API_KEY_LOCAL=${LABELBOX_TEST_API_KEY_LOCAL} \ - -e FIXTURE_PROFILE=true \ - local/labelbox-python:test pytest $(PATH_TO_TEST) - -test-staging: build-image - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="staging" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e LABELBOX_TEST_API_KEY_STAGING=${LABELBOX_TEST_API_KEY_STAGING} \ - local/labelbox-python:test pytest $(PATH_TO_TEST) - -test-staging-eu: build-image - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="staging-eu" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e LABELBOX_TEST_API_KEY_STAGING_EU=${LABELBOX_TEST_API_KEY_STAGING_EU} \ - local/labelbox-python:test pytest $(PATH_TO_TEST) - -test-prod: build-image - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="prod" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e LABELBOX_TEST_API_KEY_PROD=${LABELBOX_TEST_API_KEY_PROD} \ - local/labelbox-python:test pytest $(PATH_TO_TEST) - -test-onprem: build-image - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="onprem" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e LABELBOX_TEST_API_KEY_ONPREM=${LABELBOX_TEST_API_KEY_ONPREM} \ - -e LABELBOX_TEST_ONPREM_HOSTNAME=${LABELBOX_TEST_ONPREM_HOSTNAME} \ - local/labelbox-python:test pytest $(PATH_TO_TEST) - -test-dev0: build-image - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="custom" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e LABELBOX_TEST_API_KEY_CUSTOM=${LABELBOX_TEST_API_KEY_CUSTOM} \ - -e LABELBOX_TEST_GRAPHQL_API_ENDPOINT="https://api.dev0.na-us.lb-dev.xyz/graphql" \ - -e LABELBOX_TEST_REST_API_ENDPOINT="https://api.dev0.na-us.lb-dev.xyz/api/v1" \ - local/labelbox-python:test pytest $(PATH_TO_TEST) - -test-custom: build-image - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="custom" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e LABELBOX_TEST_API_KEY_CUSTOM=${LABELBOX_TEST_API_KEY_CUSTOM} \ - -e LABELBOX_TEST_GRAPHQL_API_ENDPOINT=${LABELBOX_TEST_GRAPHQL_API_ENDPOINT} \ - -e LABELBOX_TEST_REST_API_ENDPOINT=${LABELBOX_TEST_REST_API_ENDPOINT} \ - local/labelbox-python:test pytest $(PATH_TO_TEST) - -test-ephemeral: build-image - - @# if PATH_TO_TEST we assume you know what you are doing - @if [ -z ${PATH_TO_TEST} ]; then \ - ./scripts/ensure_local_setup.sh; \ - fi - - docker run -it --rm -v ${PWD}:/usr/src -w /usr/src \ - -e LABELBOX_TEST_ENVIRON="ephemeral" \ - -e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \ - -e SERVICE_API_KEY=${SERVICE_API_KEY} \ - -e LABELBOX_TEST_BASE_URL="http://host.docker.internal:8080" \ - local/labelbox-python:test pytest $(PATH_TO_TEST) diff --git a/README.md b/README.md index 6f9f0a85d..e94e5641e 100644 --- a/README.md +++ b/README.md @@ -8,100 +8,68 @@ [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Twitter Follow](https://img.shields.io/twitter/follow/labelbox.svg?style=social&label=Follow)](https://twitter.com/labelbox) [![LinkedIn Follow](https://img.shields.io/badge/Follow-LinkedIn-blue.svg?style=flat&logo=linkedin)](https://www.linkedin.com/company/labelbox/) +[![Supported Python Versions](https://img.shields.io/pypi/pyversions/labelbox)](https://img.shields.io/pypi/pyversions/labelbox) +Labelbox is focused on building a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. -Labelbox is a cloud-based data-centric AI platform designed to help teams create high-quality training data for their AI models. It provides a suite of tools and features that streamline the process of data curation, labeling, model output evaluation for computer vision and large language models. Visit [Labelbox](http://labelbox.com/) for more information. +Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research. - -The Python SDK provides a convenient way to interact with Labelbox programmatically, offering advantages over REST or GraphQL APIs: - -* **Simplified interactions:** The SDK abstracts away the complexities of API calls, making it easier to work with Labelbox. -* **Object-oriented approach:** The SDK provides an object-oriented interface, allowing you to interact with Labelbox entities (projects, datasets, labels, etc.) as Python objects. -* **Extensibility:** The SDK can be extended to support custom data formats and operations. +Visit [Labelbox](http://labelbox.com/) for more information. ## Table of Contents -- [Installation](#installation) -- [Code Architecture](#code-architecture) +- [Quick Start](#quick-start) - [Contribution Guidelines](#contribution-guidelines) - [Develop with AI Assistance](#develop-with-ai-assistance) - [Documentation](#documentation) -## Installation -![Supported python versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue.svg) - -Welcome to the quick start guide for integrating Labelbox into your Python projects. Whether you're looking to incorporate advanced data labeling into your workflow or simply explore the capabilities of the Labelbox Python SDK, this guide will walk you through the two main methods of setting up Labelbox in your environment: via a package manager and by building it locally. - -### Install using pip - -To get started with the least amount of hassle, follow these simple steps to install the Labelbox Python SDK using pip, Python's package manager. - -1. **Ensure pip is installed:** First, make sure you have `pip` installed on your system. It's the tool we'll use to install the SDK. - -2. **Sign up for Labelbox:** If you haven't already, create a free account at [Labelbox](http://app.labelbox.com/) to access its features. - -3. **Generate your API key:** Log into Labelbox and navigate to [Account > API Keys](https://docs.labelbox.com/docs/create-an-api-key) to generate an API key. You'll need this for programmatic access to Labelbox. - -4. **Install the SDK:** Open your terminal or command prompt and run the following command to install the Labelbox Python SDK: - - ```bash - pip install labelbox - ``` - -5. **Install optional dependencies:** For enhanced functionality, such as data processing, you can install additional dependencies with: +## Quick Start - ```bash - pip install "labelbox[data]" - ``` - - This includes essential libraries like Shapely, GeoJSON, NumPy, Pillow, and OpenCV-Python, enabling you to handle a wide range of data types and perform complex operations. - -### Building and installing locally - -For those who prefer or require a more hands-on approach, such as contributing to the SDK or integrating it into a complex project, building the SDK locally is the way to go. - - -#### Steps for local installation +### Sign Up +If you haven't already, create a free account at [Labelbox](http://app.labelbox.com/). -1. **Clone the SDK repository:** First, clone the Labelbox SDK repository from GitHub to your local machine. +### Generate an API key +Log into Labelbox and navigate to [Account > API Keys](https://docs.labelbox.com/docs/create-an-api-key) to generate an API key. -2. **Install required dependencies:** To ensure all dependencies are met, run: +### Install - ```bash - pip install -r requirements.txt - ``` +To install the SDK, run the following command. -3. **Install the SDK locally:** Navigate to the root directory of the cloned repository and run: +```bash +pip install labelbox +``` - ```bash - pip install -e . - ``` +If you'd like to install the SDK with enhanced functionality, which additional optional capabilities surrounding data processing, run the following command. - For additional data processing capabilities, remember to install the `data` extra as mentioned in the easy installation section. +```bash +pip install "labelbox[data]" +``` +### Validate Installation and API Key -## Code architecture -The Labelbox Python SDK is designed to provide a clean and intuitive interface for interacting with the Labelbox API. It primarily uses GraphQL for communication, with some REST API calls for specific functionalities. The SDK wraps the GraphQL API calls and provides object-oriented representations of Labelbox entities like projects, datasets, and labels. This allows developers to interact with Labelbox in a more Pythonic way, making code easier to read and maintain. +After installing the SDK and getting an API Key, it's time to validate them both. -Key files and classes include: +```python +import labelbox as lb -- **`labelbox/client.py`:** Contains the `Client` class, which provides methods for interacting with the Labelbox API. -- **`labelbox/orm/model.py`:** Defines the data model for Labelbox entities like projects, datasets, and labels. -- **`labelbox/schema/*.py`:** Contains classes representing specific Labelbox entities and their attributes. -- **`labelbox/data/annotation_types/*.py`:** Defines classes for different annotation types, such as bounding boxes, polygons, and classifications. -- **`labelbox/data/serialization/*.py`:** Provides converters for different data formats. +client = lb.Client(API_KEY) # API_KEY = API Key generated from labelbox.com +dataset = client.create_dataset(name="Test Dataset") +data_rows = [{"row_data": "My First Data Row", "global_key": "first-data-row"}] +task = dataset.create_data_rows(data_rows) +task.wait_till_done() +``` -The SDK wraps the GraphQL APIs and provides a Pythonic interface for interacting with Labelbox. +You should be set! Running the snippet above should create a dataset called `Test Dataset` with a single datarow with the text contents being `My First Data Row`. You can log into [Labelbox](http://labelbox.com/) to verify this. If you have any issues please file a Github Issue or contact [Labelbox Support](https://docs.labelbox.com/docs/contacting-customer-support) directly. For more advanced examples and information on the SDK, see [Documentation](#documentation) below. -## Contribution guidelines -We encourage developers to contribute to the Labelbox Python SDK and help improve its functionality and usability. Please refer to the [CONTRIB.md](CONTRIB.md) file in the root folder of the repository for detailed information on how to contribute. +## Contribution Guidelines +We encourage anyone to contribute to this repository to help improve it. Please refer to [Contributing Guide](CONTRIBUTING.md) for detailed information on how to contribute. This guide also includes instructions for how to build and run the SDK locally. ## Develop with AI assistance ### Use the codebase as context for large language models Using the [GPT repository loader](https://github.com/mpoon/gpt-repository-loader), we have created `lbx_prompt.txt` that contains data from all `.py` and `.md` files. The file has about 730k tokens. We recommend using Gemini 1.5 Pro with 1 million context length window. ## Documentation -The Labelbox Python SDK is well-documented to help developers get started quickly and use the SDK effectively. Here are some resources: +The SDK is well-documented to help developers get started quickly and use the SDK effectively. Here are some resources: -- [Official documentation](https://docs.labelbox.com/docs/overview) -- [Notebook examples](https://github.com/Labelbox/labelbox-python/tree/master/examples) +- [Labelbox Official Documentation](https://docs.labelbox.com/docs/overview) +- [Jupyter Notebook Examples](https://github.com/Labelbox/labelbox-python/tree/master/examples) - [Python SDK reference docs](https://labelbox-python.readthedocs.io/en/latest/)