Installing tensorqtl module


Festus Nyasimi


November 11, 2020

Installing tensorqtl requires pytorch which is based on gpus but there is also a cpu based version.

CRI has set up pytorch for cpus as in a conda environment and that is what I am going to use to set up tensorqtl.

I will install the tensorqtl in im-lab share space for lab use.

Steps for installation

  1. Create a directory for the environment

    mkdir -p /gpfs/data/im-lab/nas40t2/bin/envs
    cd /gpfs/data/im-lab/nas40t2/bin/envs
  2. Copy the pytorch environment into this new directory and name it tensorqtl

    cp -r /apps/software/gcc-6.2.0/miniconda3/4.7.10/envs/pytorch-1.4.0-cpu_py37 tensorqtl

    Now we have the pytorch setup environment next we are going to set up tensorqtl

  3. Checking if requirements are available

    Activate the conda environment

    conda activate /gpfs/data/im-lab/nas40t2/bin/envs/tensorqtl

    In this environment when you test the pip command its not executable because the environment has python2. We need to upgrade the environment to use python3.

  4. Test the availability of pip and python3 using the following commands

    python3 --version
    pip3 --version
    pip --version

    If you get error then you definitely need to set up these tools

  5. Set up python3

    Install python3 which works with the set up pip

    conda install python==3.8.0
  6. Install tensorqtl

    Tensorqtl is available from pip

    pip install tensorqtl

    Once installation is successful install the dependecies

  7. Install the rpy2 dependency

    conda install rpy2
  8. Test tensorqtl

     python3 -m tensorqtl --help
  9. Clean up

    Conda caches all these packages which consume a lot of disk space. The need to be removed;

     conda clean --all

NB: This environment is available for lab use. To activate the the environment for use just activate it

  conda activate /gpfs/data/im-lab/nas40t2/bin/envs/tensorqtl

Happy QTL mapping!!!