ray/doc/source/ray-air/examples/pytorch_tabular_batch_prediction.py

from typing import List
import numpy as np
import torch.nn as nn

import ray
from ray.data.preprocessors import Concatenator
from ray.train.torch import to_air_checkpoint, TorchPredictor
from ray.train.batch_predictor import BatchPredictor


def create_model(input_features: int):
    return nn.Sequential(
        nn.Linear(in_features=input_features, out_features=16),
        nn.ReLU(),
        nn.Linear(16, 16),
        nn.ReLU(),
        nn.Linear(16, 1),
        nn.Sigmoid(),
    )


dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
all_features: List[str] = dataset.schema().names
all_features.remove("target")

num_features = len(all_features)

prep = Concatenator(dtype=np.float32)

checkpoint = to_air_checkpoint(model=create_model(num_features), preprocessor=prep)
# You can also fetch a checkpoint from a Trainer
# checkpoint = best_result.checkpoint

batch_predictor = BatchPredictor.from_checkpoint(checkpoint, TorchPredictor)

predicted_probabilities = batch_predictor.predict(dataset, feature_columns=all_features)
predicted_probabilities.show()
# {'predictions': array([1.], dtype=float32)}
# {'predictions': array([0.], dtype=float32)}