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facebookresearch / labgraph

LabGraph is a Python framework for rapidly prototyping experimental systems for real-time streaming applications. It is particularly well-suited to real-time neuroscience, physiology and psychology experiments.

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LabGraph Monitor Performance Metrics - YAML generation

jfResearchEng opened this issue · comments

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🚀 Feature

Some performance metrics would be important for users to understand the real-time performance of the created graph:

  1. Latency: Average time delays between nodes; Average time delays for the critical path of the graph; Average time delays for node computation
  2. Throughput: Size of data passed per second (e.g. in bytes, kb or mb)
  3. Data rate: Number of messages passed per second

This feature would help users identified the performance bottleneck of the graph.

Users can choose in the backend whether performance monitoring is activated.

Additional context

  1. Existing application can be found [here] (https://github.com/facebookresearch/labgraph/tree/main/extensions/yaml_support)
  2. The code should be added at folder is https://github.com/facebookresearch/labgraph/tree/main/extensions/yaml_support
  3. Create setup.py and README.md, where example can be found at: https://github.com/facebookresearch/labgraph/tree/main/extensions/labgraph_viz
  4. Add github action support, reference: https://github.com/facebookresearch/labgraph/actions/workflows/main.yml
  5. Add proper license header, example:
#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.