Installation
Tutorial Video
Requirements
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
export CC_LICENSE_KEY=YOUR_LICENSE_KEY
pip install --force "clika-compression" --extra-index-url \
https://license:$CC_LICENSE_KEY@license.clika.io/simple
Making the License Key Globally Available
Since every usage of the clika-compression
package requires the availability of a valid license key, we will provide clika-compression
access to
the license key within your system.
Execute the following command from any directory:
clika-init-license
Insert your license key when prompted.
This process will generate a file containing your license key in ~/.clika/.cc_license
,
which can be accessed by the clika-compression
package, facilitating its usage whenever required.
Congratulation! you can now start using the clika-compression
python package.
(Optional) An alternative option to keep your license key globally available is to
set it as a permanent environment variable instead of using the clika-init-license
command
If you wish to add clika-compression
to a requirements.txt
file, you may add the following lines to it:
--extra-index-url https://license:$CC_LICENSE_KEY@license.clika.io/simple
clika-compression
Common Issues
CUDA related 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:
nvidia-smi
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