Tutorial 3B: Model deployment with a model's Python scoring pipeline

This tutorial will explore one of the three options available in H2O Hydrogen Torch to deploy a built model. In particular, this tutorial builds an image regression model to explore how you can deploy a model in any external Python environment using the model's standalone Python scoring pipeline.

Prerequisites

• Basic understanding of Python
• Complete Tutorial 1A: Default hyperparameter values (start your first experiment)
• Ubuntu 16.04+ with Python 3.8.

Step 1: Import dataset

For this tutorial, we will be using the preprocessed Coins image regression dataset. The dataset contains a collection of 6,028 images with one or more coins. Each image has been labeled to indicate the sum of its coins. The currency of the coins is the Brazilian Real (R$). Let's import the dataset:

1. In the H2O Hydrogen Torch navigation menu, click Import dataset.
2. In the S3 file name list, select coins_image_regression.zip.
3. Click Continue.
4. Again, click Continue.
5. Again, click Continue.

Note

As a requirement, H2O Hydrogen Torch requires the dataset for an experiment to be preprocessed to follow a certain dataset format for the problem type the experiment aims to solve. The Coins image regression dataset was preprocessed to follow a dataset format for an image regression model. To learn more, see Dataset formats.

Step 2: Build model

Let's quickly build an image regression model capable of predicting the sum of Brazilian Real (R$) coins in images. After creating the model, we will use the model's Python scoring pipeline to generate predictions (deploy the model).

1. In the H2O Hydrogen Torch navigation menu, click Create experiment.
2. In the Dataset list, select coins_image_regression.
3. In the Experiment name box, enter tutorial-3b.
4. Click Run experiment.
   Note

   After starting your experiment, H2O Hydrogen Torch will take you to the View experiments card, where you can view running and completed experiments.

Step 3: Download model's Python scoring pipeline

Now that our experiment is complete, let's download the model's Python scoring pipeline to deploy the model in a Python environment.

1. In the experiments table, click tutorial-3b.
   Note

   In the experiments table, H2O Hydrogen Torch will mark an experiment as completed when its status changes to finished.

2. Click Download scoring.

Step 4: Deploy Python scoring pipeline

The downloaded Python scoring pipeline, in particular, contains the following files:

• hydrogen_torch-*.whl: a wheel package containing the necessary H2O Hydrogen Torch framework functionality to generate predictions
• images: a folder containing sample images from the validation dataset
• scoring_pipeline.py: an example Python script demonstrating how to load the model and score new data

To deploy our model, we need to install the .whl package to an external Python environment (e.g., Conda). Right after, we will use the scoring_pipeline.py file to score new data.

Bearing in mind that you can deploy a model's Python scoring pipeline to any external Python environment of your choice (e.g, Conda), proceed with the following steps:

Caution

The H2O Hydrogen Torch Python scoring pipeline only supports Ubuntu 16.04+ with Python 3.8.

1. Create a virtual environment for Python 3.8
   Note

   • Update setuptools (pip install --upgrade pip setuptools) and pip (pip install --upgrade pip)
   • Ensure that Python 3.8-dev is installed for Ubuntu versions that support it. To install it, run: sudo apt-get install python3.8-dev
   • You need to install extra dependencies for audio models to score new data. See Deploy model.
2. Install the hydrogen_torch-*.whl package
   Note

   To install the .whl package, run: pip install *.whl

Step 5: Score new data

Now that you have deployed the built model's Python scoring pipeline, we can score on new data using the trained model weights. For instance, using the scoring_pipeline.py file, let's score the images inside the images folder.

1. Run python3 scoring_pipeline.py

   scoring_pipeline.py

   # Copyright (c) 2022 H2O.ai. Proprietary License - All Rights Reserved
   
   """scoring pipeline for models trained in H2O Hydrogen Torch."""
   
   import glob
   import json
   import os
   
   import dill
   import pandas as pd
   from torch.utils.data import DataLoader, SequentialSampler
   
   from hydrogen_torch.src.utils.modeling_utils import (
       load_checkpoint,
       run_python_scoring_inference,
   )
   
   # reading the config from trained experiment
   with open("cfg.p", "rb") as pickle_file:
       cfg = dill.load(pickle_file)
   
   # changing internal cfg settings for inference, not subject to change
   cfg.prediction._calculate_test_metric = False
   
   # preparing examplary dataframe for inference loading samples
   # this has to be altered for custom data
   
   # Image data -------------------------------------------------------
   if hasattr(cfg.dataset, "image_column"):
       images = []
       for image in sorted(glob.glob("images/*")):
           images.append(os.path.basename(image))
   
       test_df = pd.DataFrame({f"{cfg.dataset.image_column}": images})
   
       # set image folder
       cfg.dataset.data_folder_test = "images"
   # ------------------------------------------------------------------
   
   # Audio data -------------------------------------------------------
   if hasattr(cfg.dataset, "audio_column"):
       audios = []
       for audio in sorted(glob.glob("audios/*")):
           audios.append(os.path.basename(audio))
   
       test_df = pd.DataFrame({f"{cfg.dataset.audio_column}": audios})
   
       # set audio folder
       cfg.dataset.data_folder_test = "audios"
   # ------------------------------------------------------------------
   
   # Text data --------------------------------------------------------
   if hasattr(cfg.dataset, "text_column"):
       texts = []
       for text in sorted(glob.glob("texts/*")):
           texts.append(open(text).read())
   
       test_df = pd.DataFrame({f"{cfg.dataset.text_column}": texts})
   
   # special handling for span prediction problem type
   if all(
       hasattr(cfg.dataset, column) for column in ("question_column", "context_column")
   ):
       questions_and_contexts = []
   
       for text in sorted(glob.glob("texts/*")):
           data = json.load(open(text))
   
           questions_and_contexts.append(
               {
                   cfg.dataset.question_column: data["question"],
                   cfg.dataset.context_column: data["context"],
               }
           )
   
       test_df = pd.DataFrame.from_dict(questions_and_contexts)
   # ------------------------------------------------------------------
   
   # set device for inference
   cfg.environment._device = "cuda:0"
   
   # disable original pretrained weights for model initialization
   if hasattr(cfg.architecture, "pretrained"):
       cfg.architecture.pretrained = False
   
   # it is possible to specify a custom cache directory for Huggingface transformers models
   if hasattr(cfg, "transformers_cache_directory"):
       cfg.transformers_cache_directory = None
   
   # loading model and checkpoint
   model = cfg.architecture.model_class(cfg).eval().to(cfg.environment._device)
   cfg.architecture.pretrained_weights = "checkpoint.pth"
   load_checkpoint(cfg, model)
   
   # preparing torch dataset and dataloader
   # batch_size and num_workers are subject to change
   batch_size = 1 if cfg.training._single_sample_inference_batch else 16
   
   test_dataset = cfg.dataset.dataset_class(df=test_df, cfg=cfg, mode="test")
   test_dataloader = DataLoader(
       test_dataset,
       sampler=SequentialSampler(test_df),
       batch_size=batch_size,
       num_workers=4,
       pin_memory=True,
       collate_fn=cfg.dataset.dataset_class.get_validation_collate_fn(),
   )
   
   # running actual inference
   # raw_predictions is a dictionary with predictions in the raw format
   # df_predictions is a pandas DataFrame with predictions
   raw_predictions, df_predictions = run_python_scoring_inference(
       cfg=cfg, model=model, dataloader=test_dataloader
   )
   
   # final output
   print(df_predictions.head()) 

After running the scoring_pipeline.py file, we obtain the following result that you can save (e.g., save to a database or disk):

There are no columns ['label'] in the test DataFrame or they contain missing values. Not using labels.
           image_path  pred_label
0  115_1479348288.jpg  129.665771
1  150_1479430338.jpg  148.593445
2   50_1477139904.jpg   57.118023
3   50_1477282128.jpg   48.614349
4   55_1477843506.jpg   56.298607

115_1479348288.jpg

150_1479430338.jpg

50_1477139904.jpg

50_1477282128.jpg

55_1477843506.jpg

Summary

In this tutorial, we learned how to deploy a built model to any external Python environment (e.g., Conda). We also learned how to score new data using the Python scoring pipeline.

Next

Now that you know how to deploy a built model using a model's Python scoring pipeline, consider the following two tutorials to learn how to deploy a built model using the H2O Hydrogen Torch (HT) user interface (UI) or a model's H2O MLOps pipeline:

• Tutorial 1B: Model deployment through the H2O Hydrogen Torch UI
• Tutorial 2B: Model deployment with a model's H2O MLOps pipeline

To learn how to improve a built model, consider the following tutorial:

• Tutorial 2A: Model tuning with grid search

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