Running MLCube on Rivanna
In this guide, we introduce MLCube and demonstrate how to run workloads on Rivanna using the Singularity backend.
Running models consistently across platforms requires users to have commanding knowledge of the configuration of not only the source code, but also of the hardware ecosystem. It’s not uncommon that you’ll encounter a project where configuring your system to get reproducible results is error prone and time consuming, and ultimately not productive to the analyst.
MLCube(tm) is a contract-driven approach to address system configuration details and establishes a standard for generating consistent models and a mechanism for delivering these models to others, allowing others to benefit from having a solved environment.
Getting Started
First you need to install a runner for MLCube. The MLCube supports many backend runners and should run on each of them equally.
For this walkthrough, we will target the Rivanna HPC ecosystem, so we’ll leverage the lmod and singularity ecosystems.
Python install
We have two choices to install python. One is with pyenv, the other is with conda.
If you decide to install it with pyenv, use the following steps
pyenv install 3.9.7
pyenv global 3.9.7
python -m venv --prompt mlcube venv
source venv/bin/activate
python -m pip install mlcube-singularity
If you decide to install it with conda, use the following steps
conda create -n mlcube -c conda-forge python=3.9.7
conda activate mlcube
# We use pip as conda does not have an mlcube repository
python -m pip install mlcube-singularity
Note that the mlcube-singularity package can and should be installed
within your target environment.
Using MLCube
Once you have run the above commands, you will now have the MLCube script available on your path and you can now list what runners mlcube has registered with
$ mlcube config --get runners
# System settings file path = /home/<username>/mlcube.yaml
# singularity:
# pkg: mlcube_singularity
At this point you can run through any of the example projects that the mlcube project hosts at https://github.com/mlcommons/mlcube_examples.git.
Below is a set of procedures to run their hello world project.
git clone https://github.com/mlcommons/mlcube_examples.git
cd ./mlcube_examples/hello_world
mlcube run --mlcube=. --task=hello --platform=singularity
# No output expected.
mlcube run --mlcube=. --task=bye --platform=singularity
# No output expected.
cat ./workspace/chats/chat_with_alice.txt
# You should some log lines in this file.