https://github.com/guanhuawang/sensai

sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
https://github.com/guanhuawang/sensai

cifar-10 cifar-100 cifar10 cifar100 cnn-classification deep-learning deep-neural-networks distributed-deep-learning distributed-machine-learning distributed-systems imagenet imagenet1k machine-learning mlsys mobilenet-v2 resnet shufflenet-v2 sysml vgg

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sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data

• Host: GitHub
• URL: https://github.com/guanhuawang/sensai
• Owner: GuanhuaWang
• License: apache-2.0
• Created: 2019-03-05T01:11:37.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-05-03T19:58:52.000Z (5 months ago)
• Last Synced: 2024-05-03T21:05:21.679Z (5 months ago)
• Topics: cifar-10, cifar-100, cifar10, cifar100, cnn-classification, deep-learning, deep-neural-networks, distributed-deep-learning, distributed-machine-learning, distributed-systems, imagenet, imagenet1k, machine-learning, mlsys, mobilenet-v2, resnet, shufflenet-v2, sysml, vgg
• Language: Python
• Homepage: https://rise.cs.berkeley.edu/projects/sensai/
• Size: 1.26 MB
• Stars: 62
• Watchers: 9
• Forks: 8
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        


  



# sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data

## Environment

Linux, python 3.6+

## Setup

```bash

pip install -r requirements.txt

```

## Instruction

Supported CNN architectures and datasets:

| Dataset        | Architecture(`ARCH`) |

| -------------  |:-------------:|

| CIFAR-10       | vgg19_bn, resnet110, resnet164, mobilenetv2, shufflenetv2|

| CIFAR-100      | vgg19_bn, resnet110, resnet164|

| ImageNet-1K    | vgg19_bn, resnet50|

### 1. Generate class groups

   

   For CIFAR-10/CIFAR-100:

   ```bash

   python3 group_selection.py \

           --arch $ARCH \

           --resume $pretrained_model \

           --dataset $DATASET \

           --ngroups $number_of_groups \

           --gpu_num $number_of_gpu 

   ```

   For ImageNet-1K:

   ```bash

   python3 group_selection.py \

           --arch $ARCH \

           --dataset imagenet \

           --ngroups $number_of_groups \

           --gpu_num $number_of_gpu \

           --data /{path_to_imagenet_dataset}/

   ```

   

   Pruning candidate now stored in `./prune_candidate_logs/`

   

### 2. Prune models

    

   For CIFAR-10/CIFAR-100:

   ```bash

   python3 prune_and_get_model.py \

           -a $ARCH \

           --dataset $DATASET \

           --resume $pretrained_model \

           -c ./prune_candidate_logs/ \

           -s ./{TO_SAVE_PRUNED_MODEL_DIR}/

   ```

   For ImageNet-1K:

   ```bash

   python3 prune_and_get_model.py \

           -a $ARCH \

           --dataset imagenet \

           -c ./prune_candidate_logs/ \

           -s ./{TO_SAVE_PRUNED_MODEL_DIR}/ \

           --pretrained

   ```

   

   Pruned models are now saved in `./{TO_SAVE_PRUNED_MODEL_DIR}/$ARCH/`

   

### 3. Retrain pruned models

  

   For CIFAR-10/CIFAR-100:

   ```bash

   python3 retrain_grouped_model.py \

           -a $ARCH \

           --dataset $DATASET \

           --resume ./{TO_SAVE_PRUNED_MODEL_DIR}/ \

           --train_batch $batch_size \

           --epochs $number_of_epochs \

           --num_gpus $number_of_gpus

   ```

   For ImageNet-1K:

   ```bash

   python3 retrain_grouped_model.py \

           -a $ARCH \

           --dataset imagenet \

           --resume ./{TO_SAVE_PRUNED_MODEL_DIR}/ \

           --epochs $number_of_epochs \

           --num_gpus $number_of_gpus \

           --train_batch $batch_size \

           --data /{path_to_imagenet_dataset}/

   ```

   

   Retrained models now saved in `./{TO_SAVE_PRUNED_MODEL_DIR}_retrained/$ARCH/`

   

### 4. Evaluate

   For CIFAR-10/CIFAR-100:

   ```bash

   python3 evaluate.py \

           -a $ARCH \

           --dataset=$DATASET \

           --retrained_dir ./{TO_SAVE_PRUNED_MODEL_DIR}_retrained/ \

           --test-batch $batch_size

   ```

   For ImageNet-1K:

   ```bash

   python3 evaluate.py \

           -d imagenet \

           -a $ARCH \

           --retrained_dir ./{TO_SAVE_PRUNED_MODEL_DIR}_retrained/ \

           --data /{path_to_imagenet_dataset}/

   ```

## Contributors

Thanks for all the main contributors to this repository:

* [Brandon Hsieh](https://github.com/hsiehbrandon) 

* [Zhuang Liu](https://github.com/liuzhuang13)

* [Kenan Jiang](https://github.com/Kenan-Jiang) 

* [Kehan Wang](https://github.com/Jason-Khan)

* [Siyuan Zhuang](https://github.com/suquark)

And many others [Zihao Fan](https://github.com/zihao-fan), [Hank O'Brien](https://github.com/hjobrien) , [Yaoqing Yang](https://github.com/nsfzyzz), [Adarsh Karnati](https://github.com/akarnati11), [Jichan Chung](https://github.com/jichan3751), [Yingxin Kang](https://github.com/Miiira), [

Balaji Veeramani](https://github.com/bveeramani), [Sahil Rao](https://github.com/sahilrao21).

## Citation

```text

@inproceedings{wang2021sensAI,

 author = {Guanhua Wang and Zhuang Liu and Brandon Hsieh and Siyuan Zhuang and Joseph Gonzalez and Trevor Darrell and Ion Stoica},

 title = {{sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data}},

 booktitle = {Proceedings of Fourth Conference on Machine Learning and Systems (MLSys'21)},

 year = {2021}

} 

```