# energy-efficient-resilience

**Repository Path**: mirrors_ibm/energy-efficient-resilience

## Basic Information

- **Project Name**: energy-efficient-resilience
- **Description**: Training AI models to jointly optimize energy efficiency and resilience
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-08-18
- **Last Updated**: 2025-12-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

EERAI - Energy Efficient & Resilient AI
=======================================

Training AI models to jointly optimize energy efficiency and resilience.

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Required commands
-----------------

* git
* python (3.6, 3.7, 3.8, 3.9)
* virtualenv: https://virtualenv.pypa.io/en/stable/

First-time set up
-----------------

We are assuming a bash environment throughout the process. You might
try to use other shell, although some commands might need to be
modified accordingly. Execute the following commands the first
time:

```bash
git clone git@github.com:IBM/energy-efficient-resilience.git INSTALLDIRECTORY
cd INSTALLDIRECTORY
./bootstrap_environment.sh
```

Hopefully, the installation is complete. Otherwise, report the
error to the development team. The commands above did the following:
create a virtual python environment to avoid any dependency issues
(which we found quite often...) and activate it. Then, we have
checked out the repository. Finally, we installed the required
dependencies [^1].

Using EERAI
-----------

Assuming that all the variables above are set in your environment,
you just need to execute the following command to start using the
scripts.

```bash
cd INSTALLDIRECTORY
source activate_eerai
```

You will see that your command prompt changes. You should be able
to execute the provided related commands. This should be the only
command you need to execute before starting the experiments.

Files
-------
- zs_train_input_transform_single : Train on one specific perturbed model. Support normal / layerwise training.
- zs_train_input_transform_eopm : Trained by using EOPM attack. Support normal / layerwise training.
- zs_train_input_transform_mlp_eopm : (NN-based) Trained by using EOPM attack. 
- zs_train_input_transform_eopm_gen : Generator is trained by using EOPM attack. Support normal / layerwise training.
- zs_train_input_transform_adversarial : Trained by using adversarial training. Support normal / layerwise training.
- zs_train_input_transform_adversarial_gen_bit : Generator is trained by using adversarial bit error attack training. Support normal / layerwise training.
- zs_train_input_transform_mlp_adversarial : (NN-based) Train by using adversarial training. 
- zs_train_input_transform_adversarial_w : Train on clean model weights by using adversarial training. 
- zs_train_input_transform_eval : Evaluate the input transformation.

Example
-------

Check and edit the `config.py` for configuration options. Main options
of interest are the paths where all the data is going to be stored
(make sure you have enough space to avoid issues) as well as the
default layers to inject errors.

First one need to train the model:

```bash
zs_main.py resnet18 train cifar10 -E 10
```

The previous command will train the model for 10 epochs. Checkpoints
of each epoch will be stored in the configured paths. Then, one can
evaluate the model with:

```bash
zs_main.py resnet18 eval cifar10 -E 10
```

For input transformation training, one can excute the Expectation Over Perturbed Model (EOPM) to train the parameter. For LM: the lambda value between clean loss and perturbed loss. For N: how many perturbed model will be used to during training:

```bash
python zs_main.py [resnet18 | resnet50 | vgg11 | vgg16 | vgg19] transform_eopm_gen [cifar10 | gtsrb | cifar100] -ber 0.01 -cp [please input the model path here] -E 300 -LR 0.001 -BS 256 -LM 5 -N 10 -G [ConvL | ConvS | DeConvL | DeConvS | UNetL | UNetS]
```


For adversarial training, one can excute the command below:

```
python zs_main.py [resnet18 | resnet50 | vgg11 | vgg16 | vgg19] transform_adversarial_gen_bit [ cifar10 | gtsrb | cifar100] -ber 0.01 -cp [model_path] -E 300 -LR 0.001 -BS 256 -LM 5 -PGD 1 -G [ ConvL | ConvS | DeConvL | DeConvS | UNetL | UNetS ]
```

Contacts
--------

- Nandhini Chandramoorthy <Nandhini.Chandramoorthy@ibm.com>
- Ramon Bertran <rbertra@us.ibm.com>

[^1]:
    Note that the installation of pytorch and other dependencies migth require
    significant amount of space. Plan accordingly to avoid file system related
    issues.