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Version: v1.3.0

Tutorials

Learn how to train state-of-the-art deep neural networks on a large set of diverse problem types

Overview​

The H2O Hydrogen Torch tutorials are divided into two major areas:

  • Experiments: These tutorials focus on how you can build and improve a model (experiment).
  • Predictions: These tutorials focus on how you can deploy a model.

note

To learn how H2O Hydrogen Torch can help build and deploy a model with H2O Label Genie and H2O MLOps, refer to the following blog: In the H2O AI Cloud, build, deploy, and score a state-of-the-art image classification model, starting with unlabeled data.

Experiments​

  • Tutorial 1A: Default hyperparameter values (start your first experiment)

    This tutorial builds an image regression model capable of predicting the sum of Brazilian Real (R$) coins in images. While training this model, we will see how H2O Hydrogen Torch enables you to generate accurate models with expertly default hyperparameter values that H2O Hydrogen Torch derives from model training best practices used by top Kaggle grandmasters.

    Also, we will discover how H2O Hydrogen Torch enables you to monitor and understand the impact of selected hyperparameter values on the training process through simple interactive charts.

  • Tutorial 2A: Model tuning with grid search

    This tutorial explores how grid search (a feature of H2O Hydrogen Torch) can improve a built model in H2O Hydrogen Torch. Accordingly, in this tutorial, we build and improve with grid search, an image metric learning model capable of establishing similarity or dissimilarity between images of bicycles.

    Completing this tutorial should improve your understanding of how grid search can:

    1. Tune and improve models built in H2O Hydrogen Torch
    2. Expedite and eliminate the repetitive process of starting several models with different hyperparameter values
    3. Transform built models in H2O Hydrogen Torch from a good to a state-of-the-art model

Predictions​

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

    This tutorial explores one of the 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 first create a model that you will later deploy to score on new data.

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

    This tutorial explores one of the options available in H2O Hydrogen Torch to deploy a built model. In particular, this tutorial builds an image regression model to explore how it can be deployed to H2O MLOps using the model's H2O MLOps pipeline.

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

    This tutorial explores one of the 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.

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