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Latest commit @jarulraj jarulraj docs: added links to applications ... 00ee6ad Apr 30, 2023 docs: added links to applications 00ee6ad Git stats * 1,748 commits Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time .circleci ci: upgrades to ci pipeline (#684) April 30, 2023 11:49 data docs: benefit of caching and predicate reordering (#657) April 18, 2023 20:47 docker feat: GPU jenkins support (#476) November 7, 2022 11:16 docs docs: update images April 29, 2023 02:41 eva docs: minor updates April 29, 2023 10:57 script ci: upgrades to ci pipeline (#684) April 30, 2023 11:49 test fix: skip memory-intensive test April 30, 2023 12:11 third_party Refactoring Parser (#48) January 20, 2020 02:49 tutorials ci: upgrades to ci pipeline (#684) April 30, 2023 11:49 .coveragerc feat: support key-value cache for function expressions (#550) March 30, 2023 10:45 .coveralls.yml Bring in Circle CI (#141) February 21, 2021 09:14 .flake8 ci: add black to ci (#320) August 12, 2022 20:53 .gitattributes Update .gitattributes October 16, 2022 00:53 .gitignore feat: LOAD CSV Notebook (#647) April 9, 2023 18:40 CHANGELOG.md Release v0.2.1 (#670) April 25, 2023 08:54 Jenkinsfile feat: Moved to a lark-based parser (#492) December 30, 2022 16:33 Jenkinsfile_weekly feat: Moved to a lark-based parser (#492) December 30, 2022 16:33 LICENSE.txt Update README.md (#314) August 11, 2022 12:13 README.md docs: added links to applications April 30, 2023 16:01 RELEASING.md Update RELEASING.md (#408) September 28, 2022 22:04 db_api_example.py EVA Packaging Enabled (#222) January 26, 2022 10:18 pyproject.toml EVA Packaging Enabled (#222) January 26, 2022 10:18 pytest.ini chore: reducing coverage loss (#619) March 23, 2023 00:13 setup.py ci: upgrades to ci pipeline (#684) April 30, 2023 11:49 setup_git_hooks.sh Development Guide updates (#278) July 31, 2022 10:31 View code [ ] EVA AI-Relational Database System EVA is a database system for building simpler and faster AI-powered applications. Quick Links Features Demo Documentation Quick Start Illustrative Applications Traffic Analysis (Object Detection Model) MNIST Digit Recognition (Image Classification Model) Movie Emotion Analysis (Face Detection + Emotion Classfication Models) License Plate Recognition (Plate Detection + OCR Extraction Models) Meme Toxicity Classification (OCR Extraction + Toxicity Classification Models) Community and Support Contributing License README.md EVA EVA AI-Relational Database System Open EVA on Colab Slack PyPI License Python Versions Coverage Status EVA is a database system for building simpler and faster AI-powered applications. EVA is designed for supporting database applications that operate on both structured (tables, feature vectors) and unstructured data (videos, podcasts, PDFs, etc.) using deep learning models. It accelerates AI pipelines by 10-100x using a collection of optimizations inspired by time-tested relational database systems, including function caching, sampling, and cost-based predicate reordering. EVA supports an AI-oriented SQL-like query language tailored for analyzing unstructured data. It comes with a wide range of models for analyzing unstructured data, including models for image classification, object detection, OCR, text sentiment classification, face detection, etc. It is fully implemented in Python and licensed under the Apache license. Quick Links * Features * Quick Start * Documentation * Demo * Illustrative Applications * Community and Support * Contributing * License Features * Build simpler AI-powered applications using short SQL-like queries * [?][?] 10-100x faster AI pipelines using AI-centric query optimization * Save money spent on GPU-driven inference * First-class support for your custom deep learning models through user-defined functions * Built-in caching to eliminate redundant model invocations across queries * [?][?] First-class support for PyTorch and HuggingFace models * Installable via pip and fully implemented in Python Demo Here are some illustrative EVA-backed applications (all of them are Jupyter notebooks that can be opened in Google Colab): * Examining the emotion palette of actors in a movie * Analysing traffic flow at an intersection * Classifying images based on their content * Recognizing license plates * Analysing toxicity of social media memes Documentation * Detailed Documentation + If you are wondering why you might need an AI-relational database system, start with the page on Video Database Systems. + The Getting Started page shows how you can use EVA for different AI pipelines, and how you can easily extend EVA by defining an user-defined function that wraps around your custom deep learning model. + The User Guides section contains Jupyter Notebooks that demonstrate how to use various features of EVA. Each notebook includes a link to Google Colab to run the code. * Tutorials * Join Slack * Demo Quick Start * Install EVA using the pip package manager. EVA supports Python versions >= 3.7. pip install evadb * To launch and connect to an EVA server in a Jupyter notebook, check out this illustrative emotion analysis notebook: cursor = connect_to_server() * Load a video onto the EVA server (we use ua_detrac.mp4 for illustration): LOAD VIDEO "data/ua_detrac/ua_detrac.mp4" INTO TrafficVideo; * That's it! You can now run queries over the loaded video: SELECT id, data FROM TrafficVideo WHERE id < 5; * Search for frames in the video that contain a car SELECT id, data FROM TrafficVideo WHERE ['car'] <@ YoloV5(data).labels; Source Video Query Result Source Video Query Result * Search for frames in the video that contain a pedestrian and a car SELECT id, data FROM TrafficVideo WHERE ['pedestrian', 'car'] <@ YoloV5(data).labels; * Search for frames with more than three cars SELECT id, data FROM TrafficVideo WHERE ArrayCount(YoloV5(data).labels, 'car') > 3; * Use your custom deep learning model in queries with a user-defined function (UDF): CREATE UDF IF NOT EXISTS MyUDF INPUT (frame NDARRAY UINT8(3, ANYDIM, ANYDIM)) OUTPUT (labels NDARRAY STR(ANYDIM), bboxes NDARRAY FLOAT32(ANYDIM, 4), scores NDARRAY FLOAT32(ANYDIM)) TYPE Classification IMPL 'eva/udfs/fastrcnn_object_detector.py'; * Compose multiple models in a single query to set up useful AI pipelines. -- Analyse emotions of faces in a video SELECT id, bbox, EmotionDetector(Crop(data, bbox)) FROM MovieVideo JOIN LATERAL UNNEST(FaceDetector(data)) AS Face(bbox, conf) WHERE id < 15; * EVA runs queries faster using its AI-centric query optimizer. Two key optimizations are: Caching: EVA automatically caches and reuses previous query results (especially model inference results), eliminating redundant computation and reducing query processing time. Predicate Reordering: EVA optimizes the order in which the query predicates are evaluated (e.g., runs the faster, more selective model first), leading to faster queries and lower inference costs. Consider these two exploratory queries on a dataset of images: [eva_performance_comparison] -- Query 1: Find all images of black-colored dogs SELECT id, bbox FROM dogs JOIN LATERAL UNNEST(YoloV5(data)) AS Obj(label, bbox, score) WHERE Obj.label = 'dog' AND Color(Crop(data, bbox)) = 'black'; -- Query 2: Find all Great Danes that are black-colored SELECT id, bbox FROM dogs JOIN LATERAL UNNEST(YoloV5(data)) AS Obj(label, bbox, score) WHERE Obj.label = 'dog' AND DogBreedClassifier(Crop(data, bbox)) = 'great dane' AND Color(Crop(data, bbox)) = 'black'; By reusing the results of the first query and reordering the predicates based on the available cached inference results, EVA runs the second query 10x faster! Illustrative Applications Traffic Analysis (Object Detection Model) Source Video Query Result Source Video Query Result MNIST Digit Recognition (Image Classification Model) Source Video Query Result Source Video Query Result Movie Emotion Analysis (Face Detection + Emotion Classfication Models) Source Video Query Result Source Video Query Result License Plate Recognition (Plate Detection + OCR Extraction Models) Query Result Query Result Meme Toxicity Classification (OCR Extraction + Toxicity Classification Models) Query Result Query Result Community and Support If you have general questions about EVA, want to say hello or just follow along, we'd like to invite you to join our Slack Community. EVA Slack Channel If you run into any problems or issues, please create a Github issue and we'll try our best to help. Don't see a feature in the list? Search our issue tracker if someone has already requested it and add a comment to it explaining your use-case, or open a new issue if not. We prioritize our roadmap based on user feedback, so we'd love to hear from you. Contributing PyPI Version CI Status Documentation Status EVA is the beneficiary of many contributors. All kinds of contributions to EVA are appreciated. To file a bug or to request a feature, please use GitHub issues. Pull requests are welcome. For more information, see our contribution guide. License Copyright (c) 2018-present Georgia Tech Database Group. Licensed under Apache License. About AI-Relational Database System | SQL meets Deep Learning evadb.readthedocs.io/ Topics ocr database ai image-annotation yolo image-classification data-analysis eva object-detection labeling video-analytics labeling-tool serving license-plate-recognition toxic-comment-classification image-labeling-tool gpt-4 Resources Readme License Apache-2.0 license Stars 333 stars Watchers 20 watching Forks 102 forks Report repository Releases 14 v0.2.1 Latest Apr 25, 2023 + 13 releases Packages 0 No packages published Used by 8 * @georgia-tech-db * @vivek-mandal * @affan00733 * @jarulraj * @georgia-tech-db * @georgia-tech-db * @aryan-rajoria Contributors 47 * @gaurav274 * @xzdandy * @jarulraj * @saiprashanth173 * @jiashenC * @sanjanag * @jaehobang * @Abdullahshah * @IshSiva * @Anirudh58 * @aryan-rajoria + 36 contributors Languages * Python 99.1% * Other 0.9% Footer (c) 2023 GitHub, Inc. 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