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PYTHON-SETUP.md

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Software setup

The get more info about software setup and dependencies, please visit our developer doc page here.

Cerebras Wafer-Scale Cluster instructions

After installing all the Cerebras packages distributed in the CSoft platform, to support all the functionalities in Model Zoo, please install other external packages to your python environment.

For PyTorch environment, please install the packages by running:

pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu

NOTE: The rest of this guide concerns to GPU Python environment only.

Along with the Cerebras Wafer-Scale Cluster, the Model Zoo allows for models to be run on GPUs as well. To run the model code on a GPU, certain packages need to be installed. This is usually best done in a virtual environment using virtualenv or conda. We provide instructions for setting up a virtualenv in this setup instructions.

Follow along below for setting up a GPU environment setup.

GPU instructions

CUDA requirements

To run on a GPU, the CUDA libraries must be installed on the system. This includes both the CUDA toolkit as well as the cuDNN libraries. To install these packages, please follow the instructions provided on the CUDA website. And make sure to also include the cuDNN library installation.

PyTorch GPU setup

Currently, the Model Zoo only supports PyTorch version 2.0.1 which requires CUDA version 11.7/11.8.

Once all the CUDA requirements are installed, create a virtualenv on your system, with Python version 3.8 or newer, activate the virtualenv and install the packages needed for running PyTorch models using the below steps:

    virtualenv -p python3.8 /path/to/venv_gpu
    source /path/to/venv_pt/bin/activate
    pip install --upgrade pip
    pip install -r requirements.txt

To test if PyTorch is able to properly access the GPU, start a Python session through the virtual environment create above and run the following commands:

    $ source /path/to/venv_pt/bin/activate
    $ python
    >>> import torch
    >>> torch.__version__
    '2.0.1+cu117-with-pypi-cudnn' # Confirm that the PT version is `2.0.1`
    >>> torch.cuda.is_available()
    True # Should return `True`
    >>> torch.cuda.device_count()
    1 # Number of devices present
    >>> torch.cuda.get_device_name(0)
    # Should return the proper GPU type

NOTE: There is a need to install cerebras_pytorch as well. Please follow the instructions here to install cerebras_pytorch.