Highlights • Overview • Install • Getting Started • Hub • Documentation • Tutorial • Contributing • Release Notes • Blog
💭 To know more about the key tenets of GNES, read this blog post
☁️Cloud-Native & Elastic |
🐣Easy-to-Use |
🔬State-of-the-Art |
|---|---|---|
| GNES is all-in-microservice! Encoder, indexer, preprocessor and router are all running in their own containers. They communicate via versioned APIs and collaborate under the orchestration of Docker Swarm/Kubernetes etc. Scaling, load-balancing, automated recovering, they come off-the-shelf in GNES. | How long would it take to deploy a change that involves just switching a layer in VGG? In GNES, this is just one line change in a YAML file. We abstract the encoding and indexing logic to a YAML config, so that you can change or stack encoders and indexers without even touching the codebase. | Taking advantage of fast-evolving AI/ML/NLP/CV communities, we learn from best-of-breed deep learning models and plug them into GNES, making sure you always enjoy the state-of-the-art performance. |
🌌Generic & Universal |
📦Model as Plugin |
💯Best Practice |
| Searching for texts, image or even short-videos? Using Python/C/Java/Go/HTTP as the client? Doesn't matter which content form you have or which language do you use, GNES can handle them all. | When built-in models do not meet your requirments, simply build your own with GNES Hub. Pack your model as a docker container and use it as a plugin. | We love to learn the best practice from the community, helping our GNES to achieve the next level of availability, resiliency, performance, and durability. If you have any ideas or suggestions, feel free to contribute. |
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GNES Hub ship AI/ML models as Docker containers and use Docker containers as plugins. It offers a clean and sustainable way to port external algorithms (with the dependencies) into the GNES framework. GNES Hub is hosted on the Docker Hub. |
| Tag | Size and layers | Description |
|---|---|---|
{ver}-alpine |
 |
based on Alpine Linux; no deep learning libraries; extremely lightweight and portable, enables fast scaling on even edge devices. |
{ver}-buster |
 |
based on Debian 10.0; no deep learning libraries; recommended for building or extending a GNES-Hub image. |
{ver}-ubuntu18 |
 |
based on Ubuntu 18.04; no deep learning libraries. |
{ver}-full |
 |
based on Ubuntu 16.04; python-3.6.8, cuda-10.0, tf1.14, pytorch1.1, faiss, multiple pretrained models; heavy but self-contained, useful in testing GNES end-to-endly. |
| Registry | Build status |
|---|---|
Docker Hubgnes/gnes:[tag] |
 |
Github Packagedocker.pkg.github.com/gnes-ai/gnes/gnes:[tag] |
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pip install gnes[bert] | bert-serving-server>=1.8.6, bert-serving-client>=1.8.6 |
pip install gnes[flair] | flair>=0.4.1 |
pip install gnes[annoy] | annoy==1.15.2 |
pip install gnes[chinese] | jieba |
pip install gnes[vision] | opencv-python>=4.0.0, imagehash>=4.0 |
pip install gnes[leveldb] | plyvel>=1.0.5 |
pip install gnes[test] | pylint, memory_profiler>=0.55.0, psutil>=5.6.1, gputil>=1.4.0 |
pip install gnes[transformers] | pytorch-transformers |
pip install gnes[onnx] | onnxruntime |
pip install gnes[audio] | librosa>=0.7.0 |
pip install gnes[scipy] | scipy |
pip install gnes[nlp] | bert-serving-server>=1.8.6, pytorch-transformers, flair>=0.4.1, bert-serving-client>=1.8.6 |
pip install gnes[cn_nlp] | pytorch-transformers, bert-serving-client>=1.8.6, bert-serving-server>=1.8.6, jieba, flair>=0.4.1 |
pip install gnes[all] | pylint, psutil>=5.6.1, pytorch-transformers, annoy==1.15.2, bert-serving-client>=1.8.6, gputil>=1.4.0, bert-serving-server>=1.8.6, imagehash>=4.0, onnxruntime, memory_profiler>=0.55.0, jieba, flair>=0.4.1, librosa>=0.7.0, scipy, plyvel>=1.0.5, opencv-python>=4.0.0 |
