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
Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in "Feature Learning in Infinite-width Neural Networks" This repo contains code to replicate our experiments (Word2Vec, MAML) in

Edward Hu 37 Dec 14, 2022
Spearmint Bayesian optimization codebase

Spearmint Spearmint is a software package to perform Bayesian optimization. The Software is designed to automatically run experiments (thus the code n

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 1.5k Dec 29, 2022
Scripts for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation and a convolutional neural network (CNN) for image classification

About subwAI subwAI - a project for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation

82 Jan 01, 2023
FACIAL: Synthesizing Dynamic Talking Face With Implicit Attribute Learning. ICCV, 2021.

FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning PyTorch implementation for the paper: FACIAL: Synthesizing Dynamic Talking

226 Jan 08, 2023
TyXe: Pyro-based BNNs for Pytorch users

TyXe: Pyro-based BNNs for Pytorch users TyXe aims to simplify the process of turning Pytorch neural networks into Bayesian neural networks by leveragi

87 Jan 03, 2023
Out-of-Town Recommendation with Travel Intention Modeling (AAAI2021)

TrainOR_AAAI21 This is the official implementation of our AAAI'21 paper: Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, Hui Xiong

Jack Xin 13 Oct 19, 2022
The Curious Layperson: Fine-Grained Image Recognition without Expert Labels (BMVC 2021)

The Curious Layperson: Fine-Grained Image Recognition without Expert Labels Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi Code

Subhabrata Choudhury 18 Dec 27, 2022
Yolov5+SlowFast: Realtime Action Detection Based on PytorchVideo

Yolov5+SlowFast: Realtime Action Detection A realtime action detection frame work based on PytorchVideo. Here are some details about our modification:

WuFan 181 Dec 30, 2022
Backdoor Attack through Frequency Domain

Backdoor Attack through Frequency Domain DEPENDENCIES python==3.8.3 numpy==1.19.4 tensorflow==2.4.0 opencv==4.5.1 idx2numpy==1.2.3 pytorch==1.7.0 Data

5 Jun 18, 2022
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

DeeBERT This is the code base for the paper DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. Code in this repository is also available

Castorini 132 Nov 14, 2022
Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision Training Efficiency We show the training efficiency of our DSLP model b

Chenyang Huang 36 Oct 31, 2022
Fully Adaptive Bayesian Algorithm for Data Analysis (FABADA) is a new approach of noise reduction methods. In this repository is shown the package developed for this new method based on \citepaper.

Fully Adaptive Bayesian Algorithm for Data Analysis FABADA FABADA is a novel non-parametric noise reduction technique which arise from the point of vi

18 Oct 20, 2022
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
Code to compute permutation and drop-column importances in Python scikit-learn models

Feature importances for scikit-learn machine learning models By Terence Parr and Kerem Turgutlu. See Explained.ai for more stuff. The scikit-learn Ran

Terence Parr 537 Dec 31, 2022
Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training"

Saliency Guided Training Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training" by Aya Abdelsalam Ismail, Hector Cor

8 Sep 22, 2022
Novel Instances Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection

Novel Instances Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection (NimPme) The official implementation of Novel Instances Mining with

12 Sep 08, 2022
DC3: A Learning Method for Optimization with Hard Constraints

DC3: A learning method for optimization with hard constraints This repository is by Priya L. Donti, David Rolnick, and J. Zico Kolter and contains the

CMU Locus Lab 57 Dec 26, 2022
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)

Skyformer This repository is the official implementation of Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr"om Method (NeurIPS 2021).

Qi Zeng 46 Sep 20, 2022
SegNet-like Autoencoders in TensorFlow

SegNet SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a

Andrea Azzini 66 Nov 05, 2021