Features • Installation • Tutorials • Community • Citing • License

Overview

fasterAI is a PyTorch-based library that makes neural networks smaller, faster, and more efficient through state-of-the-art compression techniques. The library provides simple but powerful implementations of pruning, knowledge distillation, quantization, and other network optimization methods that can be applied with just a few lines of code.

Why compress your models with fasterai?

• Reduce model size by up to 90% with minimal accuracy loss
• Speed up inference for deployment on edge devices
• Lower energy consumption for more sustainable AI
• Simplify architectures while maintaining performance

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Features

1. Sparsification

Make your model sparse by replacing selected weights with zeros using Sparsifier or SparsifyCallback.

Parameter	Description	Options
sparsity	Percentage of weights to zero out	0-100%
granularity	Level at which to apply sparsity	'weight', 'vector', 'kernel', 'filter'
context	Scope of sparsification	'local' (per layer), 'global' (whole model)
criteria	Method to select weights	'magnitude', 'movement', 'gradient', etc.
schedule	How sparsity evolves during training	'one_shot', 'iterative', 'gradual', etc.

2. Pruning

Remove zero-weight nodes from your network structure using Pruner or PruneCallback.

Parameter	Description	Options
pruning_ratio	Percentage of weights to remove	0-100%
context	Scope of sparsification	'local' (per layer), 'global' (whole model)
criteria	Method to select weights	'magnitude', 'movement', 'gradient', etc.
schedule	How sparsity evolves during training	'one_shot', 'iterative', 'gradual', etc.

3. Knowledge Distillation

Transfer knowledge from a large teacher to a smaller student using KnowledgeDistillationCallback.

Parameter	Description	Options
teacher	Teacher model	Any PyTorch model
loss	Distillation loss function	'SoftTarget', 'Logits', 'Attention', etc.
activations_student	Student layers to match	Layer names as strings
activations_teacher	Teacher layers to match	Layer names as strings
weight	Balancing weight for distillation	0.0-1.0

4. Regularization

Push weights toward zero during training using RegularizeCallback.

Parameter	Description	Options
criteria	Regularization criteria	Same as sparsification criteria
granularity	Level of regularization	Same as sparsification granularity
weight	Regularization strength	Floating point value
schedule	How sparsity evolves during training	'one_shot', 'iterative', 'gradual', etc.
layer_types	Layer types to regularize	'nn.Conv2d', 'nn.Linear', etc.

5. Quantization

Reduce the precision of weights and activations using Quantizer or QuantizeCallback.

Parameter	Description	Options
backend	Target backend	'x86', 'qnnpack'
method	Quantization method	'static', 'dynamic', 'qat'
use_per_tensor	Force per-tensor quantization	True/False

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Quick Start

This is how easy it is to induce Sparsification in your PyTorch model:

from fasterai.sparse.all import *

learn = vision_learner(dls, model)
learn.fit_one_cycle(n_epochs, cbs=SparsifyCallback(sparsity, granularity, context, criteria, schedule))

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Installation

pip install git+https://github.com/FasterAI-Labs/fasterai.git

or

pip install fasterai

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Tutorials

• Get Started with FasterAI
• Create your own pruning schedule
• Find winning tickets using the Lottery Ticket Hypothesis
• Use Knowledge Distillation to help a student model to reach higher performance
• Sparsify Transformers
• Many more !

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Join the community

Join our discord server to meet other FasterAI users and share your projects!

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Citing

@software{Hubens,
  author       = {Nathan Hubens},
  title        = {fasterai},
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v0.1.6},
  doi          = {10.5281/zenodo.6469868},
  url          = {https://doi.org/10.5281/zenodo.6469868}
}

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License

Apache-2.0 License.