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Clement Vachet
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Add Docker deployment
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README.md
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@@ -21,21 +21,25 @@ short_description: IRIS Classification Lambda
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![example workflow](https://github.com/clementsan/iris_classification_lambda/actions/workflows/publish_docker_image.yml/badge.svg)
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![example workflow](https://github.com/clementsan/iris_classification_lambda/actions/workflows/sync_HFSpace.yml/badge.svg)
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based on measurements of physical characteristics (sepals and petals).
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- Front-end: user interface via Gradio library
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- Back-end: use of AWS Lambda function to run deployed ML model
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You can try out our deployed [Hugging Face Space](https://huggingface.co/spaces/cvachet/iris_classification_lambda
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)!
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-
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- [Local development](#1-local-development)
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- [AWS deployment](#2-deployment-to-aws)
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- [Hugging Face deployment](#3-deployment-to-hugging-face)
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## 1. Local development
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Use of Gradio library for web interface
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> export AWS_API=http://localhost:8080
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Command line for execution:
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<details>
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- Create function from container image
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Example name: ```iris-classification```
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<details>
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- Create a new ```Rest API``` (e.g. ```iris-classification-api```)
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- Add a new resource to the API (e.g. ```/classify```)
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- Add a ```POST``` method to the resource
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https://huggingface.co/spaces/cvachet/iris_classification_lambda
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Note: This space uses the ML model deployed on AWS Lambda
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![example workflow](https://github.com/clementsan/iris_classification_lambda/actions/workflows/publish_docker_image.yml/badge.svg)
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![example workflow](https://github.com/clementsan/iris_classification_lambda/actions/workflows/sync_HFSpace.yml/badge.svg)
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**Aims:** Categorization of different species of iris flowers (Setosa, Versicolor, and Virginica)
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based on measurements of physical characteristics (sepals and petals).
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**Method:** Use of Decision Tree Classifier
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**Architecture:**
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- Front-end: user interface via Gradio library
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- Back-end: use of AWS Lambda function to run deployed ML model
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You can try out our deployed [Hugging Face Space](https://huggingface.co/spaces/cvachet/iris_classification_lambda
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)!
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----
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**Table of contents:**
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- [Local development](#1-local-development)
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- [AWS deployment](#2-deployment-to-aws)
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- [Hugging Face deployment](#3-deployment-to-hugging-face)
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- [Docker Hub deployment](#4-deployment-to-docker-hub)
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----
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## 1. Local development
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Use of Gradio library for web interface
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**Note:** The environment variable ```AWS_API``` should point to the local container
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> export AWS_API=http://localhost:8080
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Command line for execution:
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<details>
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**Steps**:
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- Create function from container image
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Example name: ```iris-classification```
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<details>
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**Steps**:
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- Create a new ```Rest API``` (e.g. ```iris-classification-api```)
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- Add a new resource to the API (e.g. ```/classify```)
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- Add a ```POST``` method to the resource
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https://huggingface.co/spaces/cvachet/iris_classification_lambda
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Note: This space uses the ML model deployed on AWS Lambda
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## 4. Deployment to Docker Hub
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This web application is available on Docker Hub as a docker image
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URL:
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https://hub.docker.com/r/cvachet/iris-classification-lambda
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