ML for NLP and Computer Vision.

Overview

Katana ML Sparrow

PyPI - Python GitHub Stars GitHub Issues Current Version

Sparrow

About

Sparrow is our open-source ML product. It runs on Skipper MLOps infrastructure.

Primary focus:

  • NLP
  • Computer Vision

Sparrow containers are located in folder - services

Sparrow is in early development stage.

Author

Katana ML, Andrej Baranovskij

Update Sparrow from base Skipper MLOps infra GitHub

First time:

git remote add template https://github.com/katanaml/katana-skipper.git

Later:

git fetch template
git checkout master
git merge template/master

Enjoy!


Katana ML Skipper

PyPI - Python GitHub Stars GitHub Issues Current Version

This is a simple and flexible ML workflow engine. It helps to orchestrate events across a set of microservices and create executable flow to handle requests. Engine is designed to be configurable with any microservices. Enjoy!

Skipper

Engine and Communication parts are generic and can be reused. A group of ML services is provided for sample purposes. You should replace a group of services with your own. The current group of ML services works with Boston Housing data. Data service is fetching Boston Housing data and converts it to the format suitable for TensorFlow model training. Training service builds TensorFlow model. Serving service is scaled to 2 instances and it serves prediction requests.

One of the services, helloservice, shows how to use JavaScript based microservice with Skipper. This allows to use containers with various programming languages - Python, JavaScript, Go, Rust, Java. You can run ML services with Python frameworks, Node.js or any other choice.

Author

Katana ML, Andrej Baranovskij

Instructions

Start/Stop

Docker Compose

Start:

docker-compose up --build -d

This will start Skipper services and RabbitMQ.

Stop:

docker-compose down

Web API FastAPI endpoint:

http://127.0.0.1:8080/api/v1/skipper/tasks/docs

Kubernetes

NGINX Ingress Controller:

If you are using local Kubernetes setup, install NGINX Ingress Controller

Build Docker images:

docker-compose -f docker-compose-kubernetes.yml build

Setup Kubernetes services:

./kubectl-setup.sh

Skipper API endpoint published through NGINX Ingress (you can setup your own host in /etc/hosts):

http://kubernetes.docker.internal/api/v1/skipper/tasks/docs

Check NGINX Ingress Controller pod name:

kubectl get pods -n ingress-nginx

Sample response, copy the name of 'Running' pod:

NAME                                       READY   STATUS      RESTARTS   AGE
ingress-nginx-admission-create-dhtcm       0/1     Completed   0          14m
ingress-nginx-admission-patch-x8zvw        0/1     Completed   0          14m
ingress-nginx-controller-fd7bb8d66-tnb9t   1/1     Running     0          14m

NGINX Ingress Controller logs:

kubectl logs -n ingress-nginx -f 
   

   

Skipper API logs:

kubectl logs -n katana-skipper -f -l app=skipper-api

Remove Kubernetes services:

./kubectl-remove.sh

Components

  • api - Web API implementation
  • workflow - workflow logic
  • services - a set of sample microservices, you should replace this with your own services. Update references in docker-compose.yml
  • rabbitmq - service for RabbitMQ broker
  • skipper-lib - reusable Python library to streamline event communication through RabbitMQ
  • logger - logger service

API URLs

  • Web API:
http://127.0.0.1:8080/api/v1/skipper/tasks/docs

If running on local Kubernetes with Docker Desktop:

http://kubernetes.docker.internal/api/v1/skipper/tasks/docs
  • RabbitMQ:
http://localhost:15672/ (skipper/welcome1)

If running on local Kubernets, make sure port forwarding is enabled:

kubectl -n rabbits port-forward rabbitmq-0 15672:15672

Skipper Library on PyPI

  • PyPI - skipper-lib is on PyPI

Cloud Deployment Guides

  • OKE - deployment guide for Oracle Container Engine for Kubernetes

  • GKE - deployment guide for Google Kubernetes Engine

Usage

You can use Skipper engine to run Web API, workflow and communicate with a group of ML microservices implemented under services package.

Skipper can be deployed to any Cloud vendor with Kubernetes or Docker support. You can scale Skipper runtime on Cloud using Kubernetes commands.

IMAGE ALT TEXT

IMAGE ALT TEXT

License

Licensed under the Apache License, Version 2.0. Copyright 2020-2021 Katana ML, Andrej Baranovskij. Copy of the license.

Owner
Katana ML
Machine Learning for Business Automation
Katana ML
Unbiased Learning To Rank Algorithms (ULTRA)

This is an Unbiased Learning To Rank Algorithms (ULTRA) toolbox, which provides a codebase for experiments and research on learning to rank with human annotated or noisy labels.

71 Dec 01, 2022
Tutorials, assignments, and competitions for MIT Deep Learning related courses.

MIT Deep Learning This repository is a collection of tutorials for MIT Deep Learning courses. More added as courses progress. Tutorial: Deep Learning

Lex Fridman 9.5k Jan 07, 2023
SegNet model implemented using keras framework

keras-segnet Implementation of SegNet-like architecture using keras. Current version doesn't support index transferring proposed in SegNet article, so

185 Aug 30, 2022
Pytorch Implementation of Various Point Transformers

Pytorch Implementation of Various Point Transformers Recently, various methods applied transformers to point clouds: PCT: Point Cloud Transformer (Men

Neil You 434 Dec 30, 2022
A memory-efficient implementation of DenseNets

efficient_densenet_pytorch A PyTorch =1.0 implementation of DenseNets, optimized to save GPU memory. Recent updates Now works on PyTorch 1.0! It uses

Geoff Pleiss 1.4k Dec 25, 2022
Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)

Hierarchical Metadata-Aware Document Categorization under Weak Supervision This project provides a weakly supervised framework for hierarchical metada

Yu Zhang 53 Sep 17, 2022
Unofficial implementation of the paper: PonderNet: Learning to Ponder in TensorFlow

PonderNet-TensorFlow This is an Unofficial Implementation of the paper: PonderNet: Learning to Ponder in TensorFlow. Official PyTorch Implementation:

1 Oct 23, 2022
This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

SeBoW: Self-Born Wiring for neural trees(PaddlePaddle version) This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural

HollyLee 13 Dec 08, 2022
Repository of continual learning papers

Continual learning paper repository This repository contains an incomplete (but dynamically updated) list of papers exploring continual learning in ma

29 Jan 05, 2023
Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s

Thomas Frerix 40 Dec 17, 2022
[ECCV 2020] Gradient-Induced Co-Saliency Detection

Gradient-Induced Co-Saliency Detection Zhao Zhang*, Wenda Jin*, Jun Xu, Ming-Ming Cheng ⭐ Project Home » The official repo of the ECCV 2020 paper Grad

Zhao Zhang 35 Nov 25, 2022
TransPrompt - Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification

TransPrompt This code is implement for our EMNLP 2021's paper 《TransPrompt:Towards an Automatic Transferable Prompting Framework for Few-shot Text Cla

WangJianing 23 Dec 21, 2022
Automatically download the cwru data set, and then divide it into training data set and test data set

Automatically download the cwru data set, and then divide it into training data set and test data set.自动下载cwru数据集,然后分训练数据集和测试数据集

6 Jun 27, 2022
Mitsuba 2: A Retargetable Forward and Inverse Renderer

Mitsuba Renderer 2 Documentation Mitsuba 2 is a research-oriented rendering system written in portable C++17. It consists of a small set of core libra

Mitsuba Physically Based Renderer 2k Jan 07, 2023
Merlion: A Machine Learning Framework for Time Series Intelligence

Merlion: A Machine Learning Library for Time Series Table of Contents Introduction Installation Documentation Getting Started Anomaly Detection Foreca

Salesforce 2.8k Dec 30, 2022
Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

Gyeongjae Choi 17 Sep 23, 2021
3 Apr 20, 2022
MIM: MIM Installs OpenMMLab Packages

MIM provides a unified API for launching and installing OpenMMLab projects and their extensions, and managing the OpenMMLab model zoo.

OpenMMLab 254 Jan 04, 2023
HarDNeXt: Official HarDNeXt repository

HarDNeXt-Pytorch HarDNeXt: A Stage Receptive Field and Connectivity Aware Convolution Neural Network HarDNeXt-MSEG for Medical Image Segmentation in 0

5 May 26, 2022