End-to-end machine learning project for rices detection

Related tags

Deep Learningbasmatinet
Overview

Basmatinet

Welcome to this project folks !

Whether you like it or not this project is all about riiiiice or riz in french. It is also about Deep Learning and MLOPS. So if you want to learn to train and deploy a simple model to recognize rice type basing on a photo, then you are at the right place.

0- Project's Roadmap

This project will consist to:

  • Train a Deep Learning model with Pytorch.
  • Transfert learning from Efficient Net.
  • Data augmentation with Albumentation.
  • Save trained model with early stopping.
  • Track the training with MLFLOW.
  • Serve the model with a Rest Api built with Flask.
  • Encode data in base64 client side before sending to the api server.
  • Package the application in microservice's fashion with Docker.
  • Yaml for configurations file.
  • Passing arguments anywhere it is possible.
  • Orchestrate the prediction service with Kubernetes (k8s) on Google Cloud Platform.
  • Pre-commit git hook.
  • Logging during training.
  • CI with github actions.
  • CD with terraform to build environment on Google Cloud Platform.
  • Save images and predictions in InfluxDB database.
  • Create K8s service endpoint for external InfluxDB database.
  • Create K8s secret for external InfluxDB database.
  • Unitary tests with Pytest (Fixtures and Mocks).

1- Install project's dependencies and packages

This project was developped in conda environment but you can use any python virtual environment but you should have installed some packages that are in basmatinet/requirements.txt

Python version: 3.8.12

# Move into the project root
$ cd basmatinet

# 1st alternative: using pip
$ pip install -r requirements.txt
# 2nd alternative
$ conda install --file requirements.txt

2- Train a basmatinet model

$ python src/train.py "/path/to/rice_image_dataset/" \
                     --batch-size 16 --nb-epochs 200 \
                     --workers 8 --early-stopping 5  \
                     --percentage 0.1 --cuda

3- Dockerize the model and push the Docker Image to Google Container Registry

1st step: Let's build a docker images

# Move into the app directory
$ cd basmatinet/app

# Build the machine learning serving app image
$ docker build -t basmatinet .

# Run a model serving app container outside of kubernetes (optionnal)
$ docker run -d -p 5000:5000 basmatinet

# Try an inference to test the endpoint
$ python frontend.py --filename "../images/arborio.jpg" --host-ip "0.0.0.0"

2nd step: Let's push the docker image into a Google Container Registry. But you should create a google cloud project to have PROJECT-ID and in this case you HOSTNAME will be "gcr.io" and you should enable GCR Api on google cloud platform.

# Re-tag the image and include the container in the image tag
$ docker tag basmatinet [HOSTNAME]/[PROJECT-ID]/basmatinet

# Push to container registry
$ docker push [HOSTNAME]/[PROJECT-ID]/basmatinet

4- Create a kubernetes cluster

First of all you should enable GKE Api on google cloud platform. And go to the cloud shell or stay on your host if you have gcloud binary already installed.

# Start a cluster
$ gcloud container clusters create k8s-gke-cluster --num-nodes 3 --machine-type g1-small --zone europe-west1-b

# Connect to the cluster
$ gcloud container clusters get-credentials k8s-gke-cluster --zone us-west1-b --project [PROJECT_ID]

4- Deploy the application on Kubernetes (Google Kubernetes Engine)

Create the deployement and the service on a kubernetes cluster.

# In the app directory
$ cd basmatinet/app
# Create the namespace
$ kubectl apply -f k8s/namespace.yaml
# Create the deployment
$ kubectl apply -f k8s/basmatinet-deployment.yaml --namespace=mlops-test
# Create the service
$ kubectl apply -f k8s/basmatinet-service.yaml --namespace=mlops-test

# Check that everything is alright with the following command and look for basmatinet-app in the output
$ kubectl get services

# The output should look like
NAME             TYPE           CLUSTER-IP    EXTERNAL-IP     PORT(S)          AGE
basmatinet-app   LoadBalancer   xx.xx.xx.xx   xx.xx.xx.xx   5000:xxxx/TCP      2m3s

Take the EXTERNAL-IP and test your service with the file basmatinet/app/frontend.py . Then you can cook your jollof with some basmatinet!!!

You might also like...
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

null 78 Dec 27, 2022
Neural Dynamic Policies for End-to-End Sensorimotor Learning
Neural Dynamic Policies for End-to-End Sensorimotor Learning

This is a PyTorch based implementation for our NeurIPS 2020 paper on Neural Dynamic Policies for end-to-end sensorimotor learning.

Shikhar Bahl 47 Dec 11, 2022
[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning [CVPR'21, Oral] By Zhicheng Huang*, Zhaoyang Zeng*, Yupan H

Multimedia Research 196 Dec 13, 2022
"SOLQ: Segmenting Objects by Learning Queries", SOLQ is an end-to-end instance segmentation framework with Transformer.

SOLQ: Segmenting Objects by Learning Queries This repository is an official implementation of the paper SOLQ: Segmenting Objects by Learning Queries.

MEGVII Research 179 Jan 2, 2023
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our rece

Jaehyeon Kim 1.7k Jan 8, 2023
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

FPGA & FreeNet Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification by Zhuo Zheng, Yanfei Zhong, Ailong M

Zhuo Zheng 92 Jan 3, 2023
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

WarpDrive is a flexible, lightweight, and easy-to-use open-source reinforcement learning (RL) framework that implements end-to-end multi-agent RL on a single GPU (Graphics Processing Unit).

Salesforce 334 Jan 6, 2023
Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

CARLA-Roach This is the official code release of the paper End-to-End Urban Driving by Imitating a Reinforcement Learning Coach by Zhejun Zhang, Alexa

Zhejun Zhang 118 Dec 28, 2022
Task-based end-to-end model learning in stochastic optimization

Task-based End-to-end Model Learning in Stochastic Optimization This repository is by Priya L. Donti, Brandon Amos, and J. Zico Kolter and contains th

CMU Locus Lab 164 Dec 29, 2022
Releases(v0.2.0)

  • v0.2.0(May 26, 2022)

    We add image building annd pushing to Google Container Registry. Moreover we add a last step to deploy on a Google Kubernetes Engine cluster. And this the first official release.

    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(May 24, 2022)

Owner
Béranger
Machine Learning Engineer with high interest for Africa.
Béranger
GitHub Repository
atmaCup #11 の Public 4th / Pricvate 5th Solution のリポジトリです。

#11 atmaCup 2021-07-09 ~ 2020-07-21 に行われた #11 [初心者歓迎! / 画像編] atmaCup のリポジトリです。結果は Public 4th / Private 5th でした。 フレームワークは PyTorch で、実装は pytorch-image-m

Tawara 12 Apr 07, 2022
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

PreSumm This code is for EMNLP 2019 paper Text Summarization with Pretrained Encoders Updates Jan 22 2020: Now you can Summarize Raw Text Input!. Swit

Yang Liu 1.2k Dec 28, 2022
CM building dataset Timisoara

CM_building_dataset_Timisoara Date created: Febr-2020 The Timi\c{s}oara Building Dataset - TMBuD - is composed of 160 images with the resolution of 76

Orhei Ciprian 5 Sep 07, 2022
The source code of CVPR17 'Generative Face Completion'.

GenerativeFaceCompletion Matcaffe implementation of our CVPR17 paper on face completion. In each panel from left to right: original face, masked input

Yijun Li 313 Oct 18, 2022
The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021) Project Page | Paper Xudong Xu, Xingang Pan, Dahua Lin and Bo Dai GOF

xuxudong 97 Nov 10, 2022
Semi-Autoregressive Transformer for Image Captioning

Semi-Autoregressive Transformer for Image Captioning Requirements Python 3.6 Pytorch 1.6 Prepare data Please use git clone --recurse-submodules to clo

YE Zhou 23 Dec 09, 2022
Code Repository for Liquid Time-Constant Networks (LTCs)

Liquid time-constant Networks (LTCs) [Update] A Pytorch version is added in our sister repository: https://github.com/mlech26l/keras-ncp This is the o

Ramin Hasani 553 Dec 27, 2022
A tool for making map images from OpenTTD save games

OpenTTD Surveyor A tool for making map images from OpenTTD save games. This is not part of the main OpenTTD codebase, nor is it ever intended to be pa

Aidan Randle-Conde 9 Feb 15, 2022
A Python package to create, run, and post-process MODFLOW-based models.

Version 3.3.5 — release candidate Introduction FloPy includes support for MODFLOW 6, MODFLOW-2005, MODFLOW-NWT, MODFLOW-USG, and MODFLOW-2000. Other s

388 Nov 29, 2022
Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)

Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)

Packt 1.5k Jan 03, 2023
MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions

MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions Project Page | Paper If you find our work useful for your research, please con

96 Jan 04, 2023
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning

MaCan 4.2k Dec 29, 2022
Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

TDEER 🦌 🦒 Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) Overview TDEE

33 Dec 23, 2022
This is the implementation of our work Deep Extreme Cut (DEXTR), for object segmentation from extreme points.

This is the implementation of our work Deep Extreme Cut (DEXTR), for object segmentation from extreme points.

Sergi Caelles 828 Jan 05, 2023
Cleaned test data list of DukeMTMC-reID, ICCV2021

Cleaned DukeMTMC-reID Cleaned data list of DukeMTMC-reID released with our paper accepted by ICCV 2021: Learning Instance-level Spatial-Temporal Patte

14 Feb 19, 2022
The 2nd place solution of 2021 google landmark retrieval on kaggle.

Leaderboard, taxonomy, and curated list of few-shot object detection papers.

229 Dec 13, 2022
Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation

NorCal Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation On Model Calibration for Long-Tailed Object Detec

Tai-Yu (Daniel) Pan 24 Dec 25, 2022
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Nikolas Petrou 1 Jan 13, 2022
This repository is an unoffical PyTorch implementation of Medical segmentation in 3D and 2D.

Pytorch Medical Segmentation Read Chinese Introduction：Here！ Recent Updates 2021.1.8 The train and test codes are released. 2021.2.6 A bug in dice was

EasyCV-Ellis 618 Dec 27, 2022