microsoft/nni

Does nni contain some new pruning base methods？

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#4,567 opened on Feb 21, 2022

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Description

Describe the issue: Sorry to bother you, in nni, the current basic pruning strategies are Norm, FPGM, ActivationPruner, TaylorFOWeightPruner, etc. Are there other basic strategies, such as HRank, if I want to add it myself, where do I need to rewrite it?

Environment:

  • NNI version: V2.6
  • Training service (local|remote|pai|aml|etc): local
  • Client OS: linux
  • Server OS (for remote mode only):
  • Python version: 3.8
  • PyTorch/TensorFlow version: pytorch1.8
  • Is conda/virtualenv/venv used?: conda
  • Is running in Docker?: no

How to reproduce it?:

Contributor guide

Research direction
Study the existing pruner base classes (e.g., Norm, FPGM) in the NNI codebase, then implement a new pruner by inheriting from the base class and overriding the pruning logic.
Tech stack
pythonpytorch
Domain
machine learningai
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonPyTorchModel Pruning Concepts
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60