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feat: use output config instead of relying on leaf node type to save … #139
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Summary of ChangesHello @ChenZiHong-Gavin, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request enhances the flexibility of output management within the graph processing engine. By introducing an explicit Highlights
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Code Review
This pull request introduces a save_output configuration flag for nodes in the computation graph. This provides an explicit way to specify which node outputs should be saved, replacing the previous implicit behavior of saving outputs only for leaf nodes. The changes include updating the Node data model, modifying the execution engine to use this new flag, and updating all example configurations to be compatible with the new approach. The changes are well-implemented and make the system's behavior more predictable and configurable. I have one minor suggestion regarding import ordering for better code style.
| import ray | ||
| import ray.data | ||
| from dotenv import load_dotenv | ||
| from ray.data import DataContext |
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According to PEP 8, imports within a group should be sorted alphabetically. This improves readability and makes it easier to find imports.
| import ray | |
| import ray.data | |
| from dotenv import load_dotenv | |
| from ray.data import DataContext | |
| from dotenv import load_dotenv | |
| import ray | |
| import ray.data | |
| from ray.data import DataContext |
This pull request enhances the flexibility of output management within the graph processing engine. By introducing an explicit save_output configuration option for each node, the system no longer relies on the topological position (i.e., being a leaf node) to decide which intermediate results are preserved. This change provides developers with more precise control over data persistence, allowing them to specify exactly which node outputs are relevant for downstream use or inspection, thereby streamlining data flow and potentially optimizing resource usage.