v1.8

Table of Contents

  • Overview
  • Installation
  • QuickStart
  • Auto (Hyper-parameter) Tuning
  • Neural Architecture Search
  • Model Compression
  • Feature Engineering
  • References
  • Use Cases and Solutions
    • Automatic Model Tuning (HPO/NAS)
    • Automatic System Tuning (AutoSys)
    • Model Compression
    • Feature Engineering
    • Performance measurement, comparison and analysis
    • Use NNI on Google Colab
  • FAQ
  • How to Contribute
  • ChangeLog
  • Release 1.8 - 8/27/2020
  • Release 1.7 - 7/8/2020
NNI
  • Docs »
  • Use Cases and Solutions
  • Edit on GitHub

Use Cases and Solutions¶

Different from the tutorials and examples in the rest of the document which show the usage of a feature, this part mainly introduces end-to-end scenarios and use cases to help users further understand how NNI can help them. NNI can be widely adopted in various scenarios. We also encourage community contributors to share their AutoML practices especially the NNI usage practices from their experience.

  • Automatic Model Tuning (HPO/NAS)
    • Tuning SVD automatically
    • EfficientNet on NNI
    • Automatic Model Architecture Search for Reading Comprehension
    • Parallelizing Optimization for TPE
  • Automatic System Tuning (AutoSys)
    • Tuning SPTAG (Space Partition Tree And Graph) automatically
    • Tuning the performance of RocksDB
    • Tuning Tensor Operators automatically
  • Model Compression
    • Knowledge distillation with NNI model compression
  • Feature Engineering
    • NNI review article from Zhihu: - By Garvin Li
  • Performance measurement, comparison and analysis
    • Neural Architecture Search Comparison
    • Hyper-parameter Tuning Algorithm Comparsion
    • Model Compression Algorithm Comparsion
  • Use NNI on Google Colab
    • How to Open NNI’s Web UI on Google Colab
    • Access Web UI with frp
Next Previous

© Copyright 2020, Microsoft Revision e06a9dda.

Built with Sphinx using a theme provided by Read the Docs.