Apache Beam RunInferenceAPI example

Official Example Apache Beam RunInference API pipelines do not provide required files(e.g., input data, model file). This repository supports making the input file and saving the scikit-lean model. You can quickly try the RunInference API with only one command.

# Build the enviroment by poetry https://python-poetry.org/docs/#installation
> poetry install
# Make MNIST input dataset and model file, execute RunInferenceAPI with scikit-learn
> make run-sklearn
poetry run python beam_runinferenceapi_sample/mnist.py
INFO:__main__:Save test data as CSV format
INFO:__main__:Start Fiting
INFO:__main__:Save the scikit-learn model as pickle file
INFO:__main__:Start Prediction
Classification report for classifier SVC(gamma=0.001):
              precision    recall  f1-score   support

           0       1.00      0.99      0.99        88
           1       0.99      0.97      0.98        91
           2       0.99      0.99      0.99        86
           3       0.98      0.87      0.92        91
           4       0.99      0.96      0.97        92
           5       0.95      0.97      0.96        91
           6       0.99      0.99      0.99        91
           7       0.96      0.99      0.97        89
           8       0.94      1.00      0.97        88
           9       0.93      0.98      0.95        92

    accuracy                           0.97       899
   macro avg       0.97      0.97      0.97       899
weighted avg       0.97      0.97      0.97       899


poetry run python beam_runinferenceapi_sample/sklearn_mnist_classification.py  --input_file ./data/mnist_data.csv --output ./data/predictions.txt --model_path ./data/mnist_model_svm.pickle

Note Add PyTorch example which try only one command.