Load PyTorch model from HDFS

LoadModelFromHdfs.java

import ai.djl.Application;
import ai.djl.ModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.Classifications;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.ImageFactory;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.translate.TranslateException;

import java.io.IOException;

public class LoadModelFromHdfs {

    /*
     * 1. make sure include dependency in your project: ai.djl.hadoop:hadoop:0.14.0
     * 2. Download pytorch resnet18 model from: https://resources.djl.ai/test-models/pytorch/resnet18_jit.tar.gz
     * 3. put resnet18_jit.tar.gz file in your HDFS
     */
    public static void main(String[] args) throws IOException, ModelException, TranslateException {
        String imageFile = "https://resources.djl.ai/images/kitten.jpg";
        Image img = ImageFactory.getInstance().fromUrl(imageFile);

        Criteria<Image, Classifications> criteria =
                Criteria.builder()
                        .optApplication(Application.CV.IMAGE_CLASSIFICATION)
                        .setTypes(Image.class, Classifications.class)
                        .optModelUrls("hdfs://localhost:57545/resnet18_jit.tar.gz")
                        .optEngine("PyTorch")
                        .build();

        try (ZooModel<Image, Classifications> model = criteria.loadModel();
             Predictor<Image, Classifications> predictor = model.newPredictor()) {
            Classifications result = predictor.predict(img);
            System.out.println(result);
        }
    }
}