Clement Vachet commited on
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Add Docker deployment

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  1. README.md +19 -7
README.md CHANGED
@@ -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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- <b>Aims:</b> 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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- <b>Architecture:</b>
 
 
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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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- <b>Table of contents: </b>
 
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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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-
 
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  ## 1. Local development
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@@ -73,7 +77,7 @@ python
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  Use of Gradio library for web interface
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- <b>Note:</b> 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:
@@ -114,7 +118,7 @@ Example: ```iris-classification-lambda```
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  <details>
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- <b>Steps</b>:
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  - Create function from container image
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  Example name: ```iris-classification```
@@ -139,7 +143,7 @@ Advanced notes:
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  <details>
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- <b>Steps</b>:
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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
@@ -172,3 +176,11 @@ Hugging Face space URL:
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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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+
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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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+
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+
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+ ## 4. Deployment to Docker Hub
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+
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+ This web application is available on Docker Hub as a docker image
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+
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+ URL:
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+ https://hub.docker.com/r/cvachet/iris-classification-lambda