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sconce: Compression and Deployment made Easy

Project description

Documentation Status

Advancement of deep learning has been largely driven by the availability of large datasets and the computational power to train large models. The amount of complexity increases with each day passing and it is becoming increasingly difficult to train these models. Neverthless, infer the models efficiently on hardware.

However, the brain is able to learn from a few examples and is extremely energy efficient(Psst.. that too sparsely). Humans tend solve problems from their lens of perspective, and thus we comprehend the universe through mathematical models. One such ideation is the concept of gradient descent or other optimization techniques that we use to train our models. However, the brain does not use gradient descent to learn. It is still a mystery how the brain learns and how it is able to solve complex problems with such ease.

To bridge this gap, this package aids to perform a series of aids:

  • Make Training, Testing, Inference, Model Profiling, etc.. pipelined. Thus easing your way through research and development.

  • Compress the model through Pruning, Optimal Brain Compression, etc… This allows lesser usage of CPM(Computation, Power, Memory) and thus making it more efficient.

  • Quantize the model to make it more efficient for hardware Deployment/Inferences.

  • Leverage Sparsity in the model to make it more efficient for hardware Deployment/Inferences.

  • Deployments of the model on hardware.

  • Support Spiking Neural Networks(snnTorch) in this compression pipeline [Future integerations are expected].

  • Auto-Sensitivity Scan: Each model would require a set of ingredients of its own to make it efficient. sconce enables an auto-search algorithm that picks the best possible solution from a corpus amount of possible techniques in the fastest manner possible with the least amount of human intervention.

If you like this project, please consider starring ⭐ this repo as it is the easiest and best way to support it.

Let us know if you are using sconce in any interesting work, research or blogs, as we would love to hear more about it! If you have issues, comments, or are looking for advice on training spiking neural networks, you can open an issue, a discussion, or chat in our discord channel.

sconce Structure

sconce contains the following components:




a spiking neuron library like torch.nn, deeply integrated with autograd


Compares the performance of two PyTorch models: an original dense model and a pruned and fine-tuned model. Prints a table of metrics including latency, MACs, and model size for both models and their reduction ratios.


Currently supporting Gradual Magnitude Pruning(GMP), L1/L2 based Channel Wise Pruning(CWP), OBC, sparsegpt, etc…


Quantize the computations of the model to make it more efficient for hardware Deployment/Inferences.


Automated compression pipeline encompassing of Pruning, Quantization, and Sparsification.

sconce is designed to be intuitively used with PyTorch, compression for Linear, Convolutional and Attention blocks are supported.

At present, we are working on adding support for more compression techniques and more models. The package envisions to be a one stop solution for all your compression needs and deployed on resource constrained devices. Provided that the network models and tensors are loaded onto CUDA, sconce takes advantage of GPU acceleration in the same way as PyTorch.

sconce is a work in progress, and we welcome contributions from the community.


The following packages need to be installed to use sconce:

  • torch >= 1.1.0

  • numpy >= 1.17

  • torchprofile

  • matplotlib

  • snntorch

They are automatically installed if sconce is installed using the pip command. Ensure the correct version of torch is installed for your system to enable CUDA compatibility.


Run the following to install:

$ python
$ pip install sconce

To install sconce from source instead:

$ git clone
$ cd sconce
$ python install

API & Examples

A complete API is available here. Examples, tutorials and Colab notebooks are provided.


Open In Colab

Here are a few ways you can get started with sconce:


Define Network:

class Net(nn.Module):
    def __init__(self):
        self.conv1 = nn.Conv2d(3, 8, 3)
        self.bn1 = nn.BatchNorm2d(8)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(8, 16, 3)
        self.bn2 = nn.BatchNorm2d(16)
        self.fc1 = nn.Linear(16*6*6, 32)
        self.fc2 = nn.Linear(32, 10)

    def forward(self, x):
        x = self.pool(self.bn1(F.relu(self.conv1(x))))
        x = self.pool(self.bn2(F.relu(self.conv2(x))))
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

Make a Dict for Dataloader

image_size = 32
transforms = {
    "train": Compose([
        RandomCrop(image_size, padding=4),
    "test": ToTensor(),
dataset = {}
for split in ["train", "test"]:
  dataset[split] = CIFAR10(
    train=(split == "train"),
dataloader = {}
for split in ['train', 'test']:
  dataloader[split] = DataLoader(
    shuffle=(split == 'train'),

Define your Configurations:

# Define all parameters

from sconce import sconce

sconces = sconce()
sconces.model= Net() # Model Definition
sconces.criterion = nn.CrossEntropyLoss() # Loss
sconces.optimizer= optim.Adam(sconces.model.parameters(), lr=1e-4)
sconces.scheduler = optim.lr_scheduler.CosineAnnealingLR(sconces.optimizer, T_max=200)
sconces.dataloader = dataloader
sconces.epochs = 5 #Number of time we iterate over the data
sconces.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sconces.experiment_name = "vgg-gmp" # Define your experiment name here
sconces.prune_mode = "GMP" # Prune Mode: Currently supporting "GMP"(Supports Automated Pruning Ratio Detection), "CWP". Future supports for "OBC" and "sparseGPT"

One Roof Solution [Train -> Compress -> Deploy]:



If you’re ready to contribute to sconce, ping on discord channel.


sconce is solely being maintained by Sathyaprakash Narayanan.

Special Thanks:

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