Mlflow with Helm and serve Train Model on kubernetes

Part 1: Install Mlflow on Local Machine

Part 1: Install Mlflow on Local Machine
Part 2: Train example model and keep in Mlflow Local Machine
Part 3: Expose example api on Local Machine
Part 4: API Transform for Model API
Part 5: Install Mlflow on GKE Cluster with helm
Part 6: Keep Model in Mlflow remote Cluster
Part 7: Serve Model API on Cluster

MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions:

• Tracking experiments to record and compare parameters and results (MLflow Tracking)
• Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).
• Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
• Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry).

ref: https://www.mlflow.org/docs/latest/index.html

Install Python3.9 on WSL2 (UBuntu20.04)

• install python3.9 and pip (this style will let you to have python (python2) python3 (python 3.8) and python3.9 (python 3.9.13)

sudo apt update
sudo apt install software-properties-common
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.9
python3.9 --version

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python3.9 get-pip.py

when you use only one environment then for go on you need to use python3.9 and pip3.9 command for example

pip3.9 install -r requirements.txt

• Or you can install Conda or tool that let you have multi environments

ref: https://conda.io/projects/conda/en/latest/user-guide/install/linux.html

wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh
bash Anaconda3-2022.10-Linux-x86_64.sh
#Follow the prompts on the installer screens.
#If you are unsure about any setting, accept the defaults. You can change them later

conda create -n myenv python=3.9.5
# conda activate 
# for adtivate conda environment
conda activate myenv
# when finished, deactivate your environment with:
conda deactivate
# conda activate 
# for adtivate conda environment
#restart shell
exec $SHELL

Install Mlflow

pip install mlflow

mlflow --version

mlflow ui

or you can start with other port

mlflow server --host 0.0.0.0 -p 8889

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Credit : TrueDigitalGroup

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