- Run all the cells in the jupyter notebook
Aufgabe_A.ipynb. This will preprocess the data, train and save the model. At the end the model is evaluated on the validation set. - Now build the docker image using
bash docker build -t model-app . - Run the container using
bash docker run -p 5000:5000 -e BATCH_SIZE=16 model-app. The BATCH_SIZE is an optional environment variable that can be set by the user depending on the hosting device. It defaults to 32.
This app has two endpoints:
The predict endpoint is useful for single predictions. You give a single string as input and you expect a single sentiment prediction for that string. Send a post request to http://localhost:5000/predict with json body: {"text": YOUR_INPUT_TEXT_STRING}
The batch_predict endpoint is useful for generating predictions for a list of input texts. The predictions are generated in batches of size BATCH_SIZE internally, and then concatenated together to return a list of predicted labels of the same size of the list of input texts. Send a post request to http://localhost:5000/batch_predict with json body: {"texts": YOUR_LIST_OF_INPUT_TEXT_STRINGS}