Spaces:
Running
Running
Clement Vachet
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Parent(s):
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docs: add section about hugging face deployment
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README.md
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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 models
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## 1. Local development
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-
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bash
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> docker build -t object-detection-lambda .
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bash
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> docker run --name object-detection-lambda-cont -p 8080:8080 object-detection-lambda
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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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The Gradio web application should now be accessible at http://localhost:7860
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### 1.
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Example of a prediction request
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> --file ./tests/data/boats.jpg \
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> --model yolos-small
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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 models
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<b>Menu: </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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## 1. Local development
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### 1.1. Build and run the Docker container
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<details>
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Step 1 - Building the docker image
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bash
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> docker build -t object-detection-lambda .
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Step 2 - Running the docker container locally
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bash
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> docker run --name object-detection-lambda-cont -p 8080:8080 object-detection-lambda
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</details>
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### 1.2. Execution via user interface
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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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The Gradio web application should now be accessible at http://localhost:7860
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### 1.3. Execution via command line:
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Example of a prediction request
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> --file ./tests/data/boats.jpg \
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> --model yolos-small
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## 3. Deployment to Hugging Face
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This web application is available on Hugging Face
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Hugging Face space URL:
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https://huggingface.co/spaces/cvachet/object_detection_lambda
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Note: This space uses the ML model deployed on AWS Lambda
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