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How to build: ============================================== docker build -t <your image name> . How to run locally: ============================================== docker run -p 127.0.0.1:80:80 -d <your image name> Then use your favorite tool to connect to the end points. POST http://127.0.0.1/image with multipart/form-data using the imageData key e.g curl -X POST http://127.0.0.1/image -F imageData=@some_file_name.jpg POST http://127.0.0.1/image with application/octet-stream e.g. curl -X POST http://127.0.0.1/image -H "Content-Type: application/octet-stream" --data-binary @some_file_name.jpg POST http://127.0.0.1/url with a json body of { "url": "<test url here>" } e.g. curl -X POST http://127.0.0.1/url -d '{ "url": "<test url here>" }' For information on how to use these files to create and deploy through AzureML check out the readme.txt in the azureml directory.
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Cognitive services docker container with simple car detection model from webcam
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