Accelerate Model Training in Snowflake with H2O-3 and Driverless AI

Accelerate Model Training in Snowflake with H2O-3 and Driverless AI

Table of Contents

  1. Introduction
  2. Using the e-score UI
  3. Creating a Snow Park Container Service
  4. Running Experiments with Driverless AI
  5. Deploying a Model
  6. Leveraging H2O3 for Model Building
  7. Building Models on a Cluster
  8. Using APIs for Model Development
  9. Running Notebooks in the Environment
  10. Generating Worksheets

Using the e-score UI

The e-score UI provides a user-friendly interface for data scientists to Create and manage services within the Snow Park container services environment. To start using the e-score UI, simply click on the snowflake tab. Here, You can see the Snow Park container services that are already running in your environment.

Creating a Snow Park Container Service

If you are a data scientist visiting the e-score UI for the first time and want to create a service, click on the "Create Service" button. From there, you can select the Type of Snow Park container service you want to execute. For example, you can choose "Driverless AI" and then select a pool that allows you to allocate CPUs or GPUs. After giving your service a name, the service will be launched within the Snow Park container services.

Running Experiments with Driverless AI

Once your Snow Park container service is up and running, you can access it by clicking on the link that takes you to Driverless AI. Snowflake will prompt you to authenticate, and then you will be connected to your Driverless AI instance within the Snow Park container services. You can run experiments, iterate through different models, and utilize the full features of Driverless AI. If you have GPUs enabled, you can see that they are being utilized for each iteration of the experiment.

Deploying a Model

After finishing an experiment, you have the option to deploy the model. Snowflake offers different deployment methods, including deploying the model directly inside Snowflake as a Java UDF or using the new Snow Park container services. By selecting the deployment option for Snow Park container services, you can call the function that will execute the model with the required data. You can also request sharpness values and perform other model-related tasks.

Leveraging H2O3 for Model Building

One of the powerful features of training within Snow Park container services is the ability to leverage H2O3. You can build models using H2O3 in Snow Park container services, allowing you to utilize the capabilities of H2O3 on a cluster of nodes. This capability enables you to allocate more resources for model training and achieve better performance.

Building Models on a Cluster

When building models with H2O3 on a cluster within Snow Park container services, you can utilize all the available resources across the environment. The CPU meter shows the utilization of resources during the training process. This distributed training approach helps in training complex models faster and more efficiently.

Using APIs for Model Development

Snowflake provides an API experience for running notebooks directly within the environment. You can drive experiments for both H2O3 and Driverless AI through the API experience. This simplifies the model development process for data scientists who prefer working with APIs. The notebook link provided in the services section allows direct access to the API-driven development environment.

Running Notebooks in the Environment

In addition to using the e-score UI and API experience, data scientists can also run notebooks directly within the Snow Park container services environment. This allows for interactive data exploration, model development, and experiment management. The notebook environment integrates seamlessly with the Snow Park container services, providing a seamless experience for data scientists.

Generating Worksheets

To generate worksheets, you can select a model and request snowflake artifacts. These artifacts can include data marketplace artifacts, SQL artifacts, or any other specific artifacts you need. The worksheets can be directly generated within the Snow Park container services environment, making it convenient for further analysis or collaboration.

Conclusion

The e-score UI and Snow Park container services within Snowflake provide a comprehensive environment for data scientists to create, run, and deploy models. With features such as Driverless AI, H2O3, API support, and notebook integration, data scientists can leverage the power of distributed computing, GPUs, and advanced modeling techniques. Snowflake's seamless integration with Snow Park container services makes it a valuable platform for building, deploying, and analyzing models efficiently.

Highlights:

  • Easy-to-use e-score UI for managing Snow Park container services
  • Creating and launching Driverless AI services within Snow Park container services
  • Utilizing GPUs for faster model training and experimentation
  • Flexible model deployment options within Snowflake or Snow Park container services
  • Leveraging H2O3 for building models on a cluster of nodes
  • API support for driving experiments and model development
  • Running notebooks directly within the environment for interactive model development
  • Generating worksheets for further analysis and collaboration

FAQ:

Q: Can I choose between CPUs and GPUs when creating a Snow Park container service? A: Yes, you can select the type of service and choose the desired number of CPUs or GPUs.

Q: Are there any limitations to the number of iterations in an experiment? A: There are no specific limitations to the number of iterations in an experiment. It depends on the available resources and the complexity of the models.

Q: Can I deploy models directly within Snowflake? A: Yes, you have the option to deploy models as Java UDFs within Snowflake. Additionally, you can use the Snow Park container services for model deployment.

Q: Can I run notebooks in Snow Park container services? A: Yes, you can run notebooks directly within the Snow Park container services environment for interactive data exploration and model development.

Q: How can I leverage H2O3 for model building in Snow Park container services? A: You can build models using H2O3 on a cluster of nodes within the Snow Park container services environment. This allows you to utilize the capabilities of H2O3 and allocate more resources for model training.

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