TCLB_docker

Docker recipies for TCLB, hosted at https://hub.docker.com/u/mdzik

• cuda in image name indicate GPU support
• core images are not intendet for direct use
• buildkit images should suffice to build full-futered TCLB
• workspace CPU only are intendet for workshops, they include jupyter bindings. See also mdzik/TCLB_binder repo

Simple use

• install Singularity (https://sylabs.io/guides/3.8/user-guide/quick_start.html#quick-installation-steps)
• clone this repo

git clone https://github.com/mdzik/TCLB_docker.git
cd TCLB_docker

now you need to set path to your TCLB clone repo (cloned fork probably)

make clean
make activate
editor .local/config_all

and you are good to go

source activate workspace_cpu
cd $TCLB_PATH
scmd ./configure $CONFIGUREARGS 
tclbmake -j8 d2q9
tclb d2q9 $TCLB_PATH/example/flow/2d/karman.xml 

• Keep in mind that Singularity will include in your env directories below PWD, not necessary home.
• Jupyter Lab will do the same, so cd before scmd/jupyterlab