Developed to compress and optimize deep learning models, Nyuntam Vision provides a set of compression techniques tailored for specific deployment constraints. Users have the flexibility to choose and combine multiple techniques to achieve the best trade-off between model performance and deployment constraints. Leveraging cutting-edge techniques like pruning, quantization, distillation, etc., Nyuntam Vision achieves exceptional model compression levels on a variety of language and vision models.
Installation can be performed either by installing requirements in a virtual environment or by utilizing our docker images. To quickly run nyuntam-vision for experimentation and usage, utilize the Nyun CLI to get nyuntam-vision running in a few clicks. For contributing to nyuntam-vision, build docker containers from the available docker image or create a virtual environment from the provided requirements.txt.
To install and use Nyun CLI, head over to the Nyun CLI documentation through the link below:
Using our pre-made Docker image allows users to set up Nyuntam Vision without the hassle of installing the requirements. It can also be utilized as a development environment.
- Git clone
Clone the Nyuntam Repository - the base repository used for executing the Nyuntam Vision repository:
git clone https://github.com/nyunAI/nyuntam.git --recursive
cd nyuntam
- Docker pull
Pull our pre-made Docker image from DockerHub. The docker pull command can take a few minutes to complete and requires a stable internet connection:
docker pull nyunadmin/nyunzero_kompress_vision:v0.1
- Docker run
Create a Docker container from the downloaded image using the docker run command:
docker run -it -d --gpus all -v /dev/shm:/dev/shm -v $(pwd):/workspace --name {CONTAINER_NAME} --network=host nyunadmin/nyunzero_kompress_vision:v0.1 bash
Details about the YAML configuration can be found here.
# Sample YAML file
# General Parameters
DATASET_NAME: CIFAR10
MODEL: resnet50
JOB_PATH: 'ABC/jobs/1'
...
# Type of Compression
quant:
# Compression Algo Specific Params
EPOCHS: 1
OPTIMIZER: 'Adam'
...YAML explanation: The general-commonly used hyperparameters are specified first. We then have a subsection to specify Compression Algorithm specific parameters.
- Details of IO paths
{USER_FOLDER}
.../jobs
....../mds.xx # Output generated from the compression task
.../datasets
....../DatasetA # Folder containing datasets
.../models
....../wds.xx # Input model Weight file
.../logs
....../log.log # Log file which records job history
- Corresponding Paths in YAML:
JOB_PATH: /{USER_FOLDER}/jobs
DATASET_PATH: /{USER_FOLDER}/datasets
LOGGING_PATH: /{USER_FOLDER}/logs
- Running a Sample YAML file
# Enter Nyuntam Repository
cd nyuntam
# Run main.py file and provide the yaml configuration
python main.py --yaml_path <yaml_path>
- Details of custom dataset path
Custom datasets can be added at {USER_FOLDER}/datasets and must follow the standard format for custom datasets, which can be found in the docs. Other formats can be supported by extending the BaseDataset class.
- Details of custom model weight path
Custom weights can be added to {USER_FOLDER}/models and named wds.pt or wds.pth. The CUSTOM_MODEL_PATH hyperparameter must be updated in the YAML to point to the weight file.
For complete documentation of nyuntam-vision, visit nyun docs.