Track ML Model Training with Catalyst + neptune.ai

Table of Contents

1. Introduction
2. Importing Libraries
3. Instantiating Parameters
4. Creating the Model
5. Creating a Supervised Runner
6. Instantiating the Neptune Logger
7. Logging Metadata
8. The Levels of the Runner
9. The Experiment Level
10. The Stage Level
11. The Epoch Level
12. The Loader Level
13. Logging Checkpoints
14. Custom Dashboards
15. Stopping the Run
16. Conclusion

Neptune Catalyst Integration Explained

Neptune Catalyst Integration is a framework for machine learning tasks, developed by Neptune Act to streamlining the experimentation process by making it easier to track and log metadata, as well as Create efficient workflows. In this article, we will dive deeper into the Neptune Catalyst Integration framework and discuss the key aspects that make it a must-have tool for any machine learning project.

1. Introduction

The article will start by laying the groundwork and introducing the Neptune Catalyst Integration framework. We will highlight the key features of the framework and explain why it is such a popular choice for machine learning developers.

2. Importing Libraries

To start using Neptune Catalyst Integration framework, we need to import a few libraries. This section will cover the libraries that need to be imported and why they are essential for the proper functioning of the framework.

3. Instantiating Parameters

In this section, we will instantiate the parameters and discuss what values they can take, what they mean, and how they affect the overall performance of the machine learning models.

4. Creating the Model

In this section, the article will cover how to create a custom model using Neptune Catalyst Integration and how to select a pre-built model for the machine learning task.

5. Creating a Supervised Runner

This section will cover the creation of a supervised runner using the Neptune Catalyst Integration framework. We will also go into Detail on the different types of runners and when to use them.

6. Instantiating the Neptune Logger

In this section, we will discuss how to instantiate the Neptune logger and pass in the API token, project name, and tags.

7. Logging Metadata

Here, we will highlight the importance of logging metadata and discuss how to log metadata using the Neptune logger.

8. The Levels of the Runner

In this section, the article will discuss the different levels of the runner and how metadata is being logged at each level - the experiment level, the stage level, the epoch level, and the loader level.

9. The Experiment Level

We will discuss what the experiment level is, its importance in the Neptune Catalyst Integration framework, and how the metadata is being logged at this level.

10. The Stage Level

This section will cover the stage level, what the stages are, and how metadata is being logged at this level.

11. The Epoch Level

In this section, we will discuss what the epoch level is and how metadata is being logged at this level.

12. The Loader Level

Here, we will explain what the loader level is and how metadata is being logged at this level.

13. Logging Checkpoints

In this section, we will explain how to log checkpoints using the Neptune logger.

14. Custom Dashboards

We will discuss the concept of custom dashboards and how they allow us to select specific metadata that we logged and want to see all in one place.

15. Stopping the Run

Finally, this section will cover how to stop the run, what happens when the run is stopped, and how to access the run data after it has ended.

16. Conclusion

In this concluding section, we will summarize the key points covered in the article, emphasize the importance of using the Neptune Catalyst Integration framework, and encourage readers to give it a try.

Highlights

• Neptune Catalyst Integration is a framework for machine learning tasks, developed by Neptune Act.
• The framework streamlines the experimentation process by making it easier to track and log metadata and create efficient workflows.
• The article covers the different levels of the runner and how metadata is being logged at each level.
• Custom dashboards allow selecting specific metadata that we logged and want to see all in one place.

FAQ Q&A

1. What is Neptune Catalyst Integration?
   • Neptune Catalyst Integration is a framework for machine learning tasks, developed by Neptune Act, to streamline the experimentation process by making it easier to track and log metadata and create efficient workflows.
2. How do I log metadata using Neptune Catalyst Integration?
   • To log metadata using Neptune Catalyst Integration, You need to instantiate the Neptune logger and pass in the API token, project name, and tags.
3. What are custom dashboards in Neptune Catalyst Integration?
   • Custom dashboards are a feature of Neptune Catalyst Integration that allows selecting specific metadata that we logged and want to see all in one place.