Installation

The code is developed and tested on a machine with 8 Nvidia GPUs with the CentOS Linux 7.4 (Core) operation system.

Note

We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment and add the required packages to the environment.

Please follow the steps below for a successful installation:

1. Create a new conda environment:

   $ conda create -n py3_torch python=3.7
   $ source activate py3_torch
   $ conda install pytorch torchvision cudatoolkit=9.2 -c pytorch
   

2. Ensure that at least PyTorch 1.3.0 is installed:

   $ python -c 'import torch; print(torch.__version__)'
   >>> 1.3.0
   

3. Ensure CUDA is setup correctly (optional):

   1. Check if PyTorch is installed with CUDA support:

      $ python -c 'import torch; print(torch.cuda.is_available())'
      >>> True
      

   2. Add CUDA to $PATH and $CPATH (note that your actual CUDA path may vary from /usr/local/cuda):

      $ PATH=/usr/local/cuda/bin:$PATH
      $ echo $PATH
      >>> /usr/local/cuda/bin:...
      
      $ CPATH=/usr/local/cuda/include:$CPATH
      $ echo $CPATH
      >>> /usr/local/cuda/include:...
      

   3. Verify that nvcc is accessible from terminal:

      $ nvcc --version
      >>> 9.2
      

   4. Ensure that PyTorch and system CUDA versions match:

      $ python -c 'import torch; print(torch.version.cuda)'
      >>> 9.2
      
      $ nvcc --version
      >>> 9.2
      

4. Download and install the package:

   $ git clone git@github.com:zudi-lin/pytorch_connectomics.git
   $ cd pytorch_connectomics
   $ pip install -r requirements.txt
   $ pip install --editable .
   

Note

If you meet compilation errors, please check the TROUBLESHOOTING.md. It is highly recommended to first play with the demo scripts to make sure that the installation is correct and also have intial taste of the modules.