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MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow can be used with many popular ML frameworks including:
  • Scikit-Learn
  • Keras
  • Tensorflow
  • Pytorch
MLflow can track your experimental runs to create a repeatable auditable registry of models.

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Quick start guide

  1. Prerequisites
  2. Introduction
  3. MLflow UI server
  4. MLflow runner for training ML models

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Prerequisites

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Introduction

In this deployment of MLflow we will set up one CUDO Compute virtual machine to serve the MLflow UI/Web app and store models and metrics from runs. We will then use a second CUDO Compute virtual machine to perform training, you can run as many of these as you like concurrently. They only need to run for the duration of training. Optionally you can use your local machine to run the web app if you are able to configure your network so that you have a port publicly accessible.

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MLflow UI server

Start a virtual machine on CUDO Compute, this can be CPU only no GPU. Use the Ubuntu Minimal 20.04 image. This virtual machine should remain running for the duration of your work. Pick something with 8GB RAM or more. Get the IP address of the virtual machine. Enter replace the address in tracking_ip below with the IP address of the virtual machine and then run the commands below. 
tracking_ip=xx.xx.xx.xx \
tracking_port=5000 \
ssh -o "StrictHostKeyChecking no" root@$tracking_ip << EOF
apt-get update
apt-get install lsof
DEBIAN_FRONTEND=noninteractive apt-get install python3.10 python3-pip -y
which python
pip install click==8.0 'urllib3<=1.25'
pip install mlflow
kill $(lsof -t -i:$tracking_port)
mlflow server --host $tracking_ip --port $tracking_port --backend-store-uri sqlite:///mlruns.db --default-artifact-root ./mlruns &
EOF
All of your data is stored in ~/mlruns directory and ~/mlruns.db file MLflow UI server on a local machine Make sure port 5000 of your local machine is publicly accessible. 
conda create mlflow_env
conda activate mlflow_env
conda install -c conda-forge mlflow -y
mlflow server --host PUBLIC_IP_ADDRESS --port 5000

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MLflow runner for training ML models

Start another virtual machine on CUDO Compute, this can be CPU only or a GPU machine. Use the Ubuntu 22.04 + NVIDIA drivers + Docker image. The script below pulls a docker container for MLflow, then MLflow pulls a GitHub repository and runs it. The GitHub repository is configured with MLflow projects. So when MLflow runs it creates a conda environment and installs the necessary python packages. Then it runs the model training. The training script logs its output to the MLFLOW_TRACKING_URI. Get the IP address from your CUDO Compute virtual machine that is used for training and replace runner_ip with it Get the IP address from your CUDO Compute virtual machine that is used for the MLFlow UI and replace tracking_ip with it

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CPU only

tracking_ip=xx.xx.xx.xx \
tracking_port=5000 \
runner_ip=yy.yy.yy.yy \
ssh -o "StrictHostKeyChecking no" root@$runner_ip << EOF
docker run --rm -e MLFLOW_TRACKING_URI=http://$tracking_ip:$tracking_port \
cudoventures/mlflow-runner \
mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=5.0
EOF

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GPU

tracking_ip=xx.xx.xx.xx \
tracking_port=5000 \
runner_ip=yy.yy.yy.yy \
ssh -o "StrictHostKeyChecking no" root@$runner_ip << EOF
docker run --gpus all --rm -e MLFLOW_TRACKING_URI=http://$tracking_ip:$tracking_port \
cudoventures/mlflow-runner \
mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=5.0
EOF
Go to http://tracking_ip:5000 to see the MLflow UI, you should be able to see your training results.