The Machine Learning Container Creator (MLCC) is an experimental tool and a work-in-progress. MLCC facilitates running machine learning frameworks and applications in Red Hat Enterprise Linux environments by semi-automating the generation of dockerfiles which can be used to build container images with required dependencies to run machine learning software. Using containers to run ML applications and explore ML environments is a good idea because doing that can hide messy details and can help preserve your host OS instance from accidental corruption.
Your RHEL container host system must have a valid RHEL subscription, and you should enable these repos:
subscription-manager repos --enable="rhel-7-server-rpms" --enable="rhel-7-server-optional-rpms" --enable="rhel-7-server-extras-rpms"
You can get a free RHEL developer subscription at https://developers.redhat.com/products/rhel/download
To install EPEL, do:
yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
To install NVIDIA's CUDA stack see: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
Some gtk packages are needed to build the MLCC GUI:
yum install gnome-devel-docs gtk+ gtk3-devel gtk3-devel-docs gtk+-devel gtk-doc libcanberra-gtk3
Compile MLCC with GUI:
gcc -std=gnu99 -g -Wall -o mlcc mlcc.c -DGUI `pkg-config --cflags gtk+-3.0` `pkg-config --libs gtk+-3.0`
Use MLCC via GUI:
mlcc -G
(There might be some harmless gtk messages if there are missing fonts, etc)
Use MLCC via interactive terminal menus:
mlcc -I
Use MLCC via "-i" interface:
mlcc -i RHEL7.5,CUDA9.2,Pandas,TensorFlow,Keras
With the Makefile you can use:
make test
to test build dozens of various container imagess. You can also use something like:
make test SUBSET=tensorflow
to selectively build subsets of the MLCC sample test commands. Note that building many container images from source can take many hours to complete. After the images are initially built, the containers should start up in a fraction of a second.
Use podman for building and running containers. See: https://podman.io and the podman scripts.