Environments#

damask_parse#

We used our CentOS docker image (orgs/hpcflow) to produce a “relocatable” conda environment for the damask_parse MatFlow environment, using conda-pack. Using the CentOS image is required because of glibc compatibilities.

In the container:

  • Install Miniconda via the bash installation script: https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html

  • Initialise conda for use in the shell: conda init

  • Reload .bashrc: source ~/.bashrc

  • Install conda pack: conda install conda-pack

  • Create a new conda environment that contains damask-parse and matflow: conda create -n matflow_damask_parse_v3a7_env python=3.10

  • Install libGL for VTK (required by the damask python package) yum install mesa-libGL

  • Activate the environment: conda activate matflow_damask_parse_v3a7_env

  • Add packages via pip: pip install matflow-new damask-parse

  • Deactivate the environment: conda deactivate

  • Pack the environment into a tarball: conda pack matflow_damask_parse_v3a7_env

  • Save the resulting compressed file outside of the container and transfer to the target machine

On the target machine:

  • Unpack the environment:

    mkdir matflow_damask_parse_v3a7_env
tar -xzf matflow_damask_parse_v3a7_env.tar.gz -C matflow_damask_parse_v3a7_env

  • Activate the environment: source matflow_damask_parse_v3a7_env/bin/activate

  • Run: conda-unpack

  • The environment can now be activated as normal using the source command above.

Resources:

Example environment definition#

name: damask_parse_env
setup: |
  conda activate matflow_damask_parse_env
executables:
  - label: python
    instances:
      - command: python
        num_cores: 1
        parallel_mode: null

damask#

Example environment definition#

name: damask_env
executables:
  - label: damask_grid
    instances:
      - command: docker run --rm --interactive --volume ${PWD}:/wd --env OMP_NUM_THREADS=1 eisenforschung/damask-grid:3.0.0-alpha7
        parallel_mode: null
        num_cores: 1