Method 3: Running Inference on Each Module Separately

method_3.py

import json
from pprint import pprint

import pandas as pd
import torch

from ludwig.utils.inference_utils import to_inference_module_input_from_dataframe

preprocessor = torch.jit.load(
    f"{EXPERIMENT_DIRECTORY}/torchscript/inference_preprocessor.pt")
predictor = torch.jit.load(
    f"{EXPERIMENT_DIRECTORY}/torchscript/inference_predictor-cpu.pt")
postprocessor = torch.jit.load(
    f"{EXPERIMENT_DIRECTORY}/torchscript/inference_postprocessor.pt")

input_df = pd.read_parquet(f"{DATA_DIRECTORY}/twitter_bots.parquet")
input_sample_df = input_df.head(2)

with open(
    f"{EXPERIMENT_DIRECTORY}/torchscript/model_hyperparameters.json") as f:
    config = json.load(f)

input_sample_dict = to_inference_module_input_from_dataframe(
    input_sample_df, config)

preproc_input = preprocessor(input_sample_dict)
raw_output = predictor(preproc_input)
postproc_output = postprocessor(raw_output)
pprint(postproc_output)