Streamline Your Pipeline: Kedro + Neptune Integration

Streamline Your Pipeline: Kedro + Neptune Integration

Table of Contents:

1. Introduction
2. Using Cadro Neptune plugin to log pipeline metadata
3. Setting up Neptune and initializing it at the project level
4. Logging custom metrics and visualizations
5. Viewing metadata in the Neptune UI
6. Advanced example: Training multiple models and combining them
7. Grouping and comparing pipeline executions
8. Analyzing different pipeline nodes and their outputs
9. Creating custom dashboards in Neptune
10. Conclusion

Introduction

In this article, we will explore how to use the Cadro Neptune plugin to log metadata about your Cadro pipeline runs into Neptune, and later browse and filter the runs in the Neptune UI. We will cover the setup process, logging custom metrics and visualizations, viewing metadata in the UI, advanced examples with multiple models, grouping and comparing pipeline executions, analyzing different pipeline nodes, and creating custom dashboards in Neptune.

Using Cadro Neptune plugin to log pipeline metadata

To get started, You need to install the Cadro Neptune plugin and initialize it at the project level. By connecting to Neptune, you can log all the metadata you want, including custom metrics and visualizations, with minimal effort. This allows for easy comparison and filtering of pipeline executions.

Setting up Neptune and initializing it at the project level

To connect to Neptune, you'll need to provide your API token and project information. The Cadro Neptune plugin will Create a few files, including a Neptune YAML file and a credentials file. By passing the appropriate arguments during initialization, you can securely connect to Neptune and start logging pipeline metadata.

Logging custom metrics and visualizations

Once Neptune is connected, you can log custom metrics and visualizations from specific nodes in your pipeline. By adding an argument for the Neptune run handler and using the net_to_run.log() function, you can easily save and upload important information, such as accuracy scores and confusion matrices, to Neptune. These metrics and visualizations will be visible in the Neptune UI.

Viewing metadata in the Neptune UI

In the Neptune UI, you can browse and view all the metadata that has been logged from your Cadro pipeline runs. The UI provides a custom dashboard where you can see the logged information, including accuracy scores, confusion matrices, parameters, file paths, and more. Additionally, the folder structure allows you to navigate through different namespaces and explore the metadata in more Detail.

Advanced example: Training multiple models and combining them

In an advanced example, we will demonstrate how to train multiple models and combine them in your pipeline. By logging the metadata from these models, such as accuracy scores, ROC curves, and precision-recall curves, you can compare and analyze the performance of different models. This allows for deeper insights into your pipeline executions and helps in making informed decisions.

Grouping and comparing pipeline executions

By using grouping and filtering functionalities in Neptune, you can easily compare different pipeline executions. You can group runs Based on data versions, parameters, or any other criteria. This provides a convenient way to analyze the impact of different variables on the pipeline's performance and make data-driven decisions.

Analyzing different pipeline nodes and their outputs

Neptune allows you to analyze and compare the outputs of different nodes in your pipeline. By using Parallel coordinates and customizable table views, you can easily Visualize and evaluate the differences between pipeline nodes. This helps in identifying Patterns, finding optimal configurations, and understanding the impact of each node on the overall pipeline.

Creating custom dashboards in Neptune

Neptune provides the flexibility to create custom dashboards to suit your specific needs. You can add columns, color code data, and save views for quick access. This allows you to create personalized visualizations of the logged metadata, enabling you to monitor and analyze the pipeline's performance at a glance.

Conclusion

Using the Cadro Neptune plugin, you can seamlessly log and analyze metadata about your Cadro pipeline runs in Neptune. By leveraging Neptune's UI features, such as custom dashboards, grouping, and comparisons, you can gain deeper insights into your pipeline's performance and make informed decisions. With Neptune's powerful capabilities, you can optimize your pipeline and streamline your data analysis process.

Highlights:

• Seamlessly log metadata from Cadro pipeline runs into Neptune
• Log custom metrics and visualizations with minimal effort
• View and analyze logged metadata in the Neptune UI
• Compare and analyze multiple pipeline executions
• Analyze and compare the outputs of different pipeline nodes
• Create custom dashboards in Neptune for personalized visualizations

FAQ:

Q: What is Cadro Neptune? A: Cadro Neptune is a plugin that allows you to log metadata from your Cadro pipeline runs into Neptune and analyze the data in Neptune's UI.

Q: Can I log custom metrics and visualizations? A: Yes, you can log custom metrics and visualizations from specific nodes in your pipeline using the Cadro Neptune plugin.

Q: How can I compare different pipeline executions? A: Neptune provides grouping and filtering functionalities that allow you to compare and analyze different pipeline executions based on various criteria.

Q: Can I create custom dashboards in Neptune? A: Yes, you can create custom dashboards in Neptune to visualize and analyze the metadata according to your specific needs.

Q: Is Neptune suitable for advanced pipeline configurations? A: Yes, Neptune is suitable for advanced pipeline configurations, such as training multiple models and combining them. You can log and analyze the metadata from these configurations in Neptune.

Q: How does Neptune help optimize the pipeline? A: By logging and analyzing metadata in Neptune, you can identify patterns, compare different configurations, and make data-driven decisions to optimize your pipeline's performance.