Tutorial 1B: Model deployment through the H2O Hydrogen Torch UI

This tutorial explores one of the three options available in H2O Hydrogen Torch to deploy a built model. In particular, this tutorial explores how to deploy a built model through the H2O Hydrogen Torch (HHT) user interface (UI). This tutorial also explores downloading the generated predictions (in HHT) and how we can view the downloaded predictions using Python. At a high level, we will first create a model that you will later deploy to score on new data.

Prerequisites

• Basic understanding of Python
• Complete Tutorial 1A: Default hyperparameter values (start your first experiment)

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 H2O Hydrogen Torch UI 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-1b.
4. Click Run experiment.

Note

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

Step 3: Deploy built model

Now that our model (experiment) is complete, let's deploy our image regression model through the H2O Hydrogen Torch UI. In particular, to score new data, let's use the test dataframe included in the coins_image_regression dataset.

Note

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

1. In the H2O Hydrogen Torch navigation menu, click Predict data.

Before using our built model to generate predictions for a test dataset, observe the settings in the Predict data card, organized in the following three sections: Dataset, Prediction, and Environment. These settings control the dataset to use for the prediction, aspects of the prediction, and the environment H2O Hydrogen Torch runs predictions. For this tutorial, H2O Hydrogen Torch has autoselected a value for the settings in the Predict data card.

Note

To learn more about the prediction settings, see Prediction settings.

1. In the Experiment list, select tutorial-1b.
2. In the Prediction name box, enter tutorial-1b.
3. Click Run predictions.
   Note

   After running your predictions, H2O Hydrogen Torch will take you to the predictions table, where you can view running and completed predictions.

Step 4: Download predictions

Now that our prediction is complete, let's download it.

1. In the predictions table, click tutorial-1b.
   Note

   In the predictions table, H2O Hydrogen Torch will mark a prediction as completed when its status changes to finished.

2. Click Download predictions.
   Note

   • Downloaded predictions come in a .zip file. The .zip file contains the following files:
     • A test_predictions.csv file
       • The .csv file is a structured dataframe with final predictions for the provided test dataframe. In particular to our downloaded predictions, the .csv file contains:
         • All the N columns in the test dataframe
         • A column name pred_{label_column_name} that contains probabilities for the label column, label_column_name refers to the label column name found in the train (test) dataframe
     • A test_raw_predictions.pkl file
       • The .pkl file is a pickled Python dictionary with raw predictions for the provided test dataframe. In particular to our downloaded predictions, its .pkl file contains:
         • predictions
           • A 2-dimensional NumPy array that contains label predictions. The shape of the array is (n, m), where n represents the number of observations, while m represents the number of labels.
         • labels
           • A 1-dimensional NumPy array that contains label names.
         • {image_column}
           • A 1-dimensional NumPy array that contains input image names. The name of the key is {image_column}, where image_column refers to the name of the image column in the train (test) dataframe.
   • The structure of the files in the .zip file is different among all supported problem types. To learn more, see Prediction files formats.

Step 5: View predictions

Using Python, we can view the downloaded .csv and .pkl file as follows; in particular, let's observe the first prediction stored in both files:

import pickle
import pandas as pd

df = pd.read_csv('test_predictions.csv')

with open('test_raw_predictions.pkl', 'rb') as f:
    out = pickle.load(f)

print(df.head(1))

           image_path  label  fold  pred_label
0  105_1479344562.jpg    105     1  104.038216

print(out.keys())

dict_keys(['predictions', 'labels', 'image_path'])

print(f'Prediction: {out["predictions"][0]}')
print(f'image_path: {out["image_path"][0]}')

Prediction: [104.038216]
image_path: 105_1479344562.jpg

105_1479344562.jpg

Summary

In this tutorial, we learned how to deploy a built model through the H2O Hydrogen Torch (HT) user interface (UI). Mainly we explored the collection of settings available to you to score new data on the UI. Right after, we downloaded the generated predictions to view them using Python.

Next

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

• Tutorial 2B: Model deployment with a model's H2O MLOps pipeline
• Tutorial 3B: Model deployment with a model's Python scoring pipeline

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

• Tutorial 2A: Model tuning with grid search

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