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Version: 0.2


Tutorial Video


We recommend using a conda environment manager.

System Requirements:

  • Python>=3.8
  • CUDA (run nvidia-smi command and make sure it shows your available GPUs)

Optional Requirements

If you wish to visualize the model architecture graph before and after the compression process you can install the dependencies graphviz and pydot using:

conda install graphviz 
pip install pydot

Once the dependencies are installed, two .png files containing the visualizations of the original model and the quantized model will be generated in the outputs folder when running CLIKA Compression Operation (CCO).

For more information, see graph visualization.

CLIKA Compression Installation

Installing clika-compression is like installing any other Python package using PIP package manager; the only special requirement is to add the license key to gain access to CLIKA's PIP index.

You can install the package using the following lines:

# set your license key as a environment varialbe 
pip install --force "clika-compression" --extra-index-url \

Making the License Key Globally Available

Since every usage of the clika-compression package requires the availability of a valid license key, we will provide clika-compression access to the license key within your system.

Execute the following command from any directory:


Insert your license key when prompted.

This process will generate a file containing your license key in ~/.clika/.cc_license, which can be accessed by the clika-compression package, facilitating its usage whenever required.

Congratulation! you can now start using the clika-compression python package.


(Optional) An alternative option to keep your license key globally available is to set it as a permanent environment variable instead if using the clika-init-license command


If you wish to add clika-compression to a requirements.txt file, you may add the following lines:

--extra-index-url https://license:$

Common Issues

In the case of CUDA related issues we recommend to performing the following steps

Test nvidia-smi

Validate CUDA Installation and make sure it is compatible with your PyTorch installation by running the following command:


Expected output:

  • A detailed view of all the available GPUs in your system.

If the command does not work as expected please refer to the CUDA installation guide.

Test PyTorch GPU access

Test if the GPU is accessible to PyTorch using:

python3 -c "import torch; print(torch.cuda.is_available())" 

Expected output:

>>> True

If the output is False please refer to the CUDA installation guide