Tracking Machine Learning Experiments with ML Flow

How-to-Guide for setting up and connecting to ML Flow, tracking experiments and analysing them.

1) Install MLflow to lukaenv (conda activate your-custom-environment)

pip install mlflow

2) Run the following commands to setup a server on localhost:

mlflow server --host 0.0.0.0 --port=5001

3) Run the MLflow version of the autoencoder (or setup your own experimental codes with whatever you want to track; see quick instructions “How to use MLflow” below)

4) Create a tunnel from the cluster localhost:5000 to your own localhost port to view and compare the experiments through your own browser:

  
  Run the following code on your computers command prompt to map the localhost ports:
  
  <code>
  ssh -L localhost:5000:130.208.209.2:5001 matiasrus@130.208.209.2
  </code>
  

5) Open localhost:5000 through your browser

1) Configure run in the beginning of training code:

import mlflow

mlflow_tracking_uri = 'http://localhost:5000'
mlflow_experiment = 'test'

mlflow.set_tracking_uri(mlflow_tracking_uri)
mlflow.set_experiment(mlflow_experiment)

2) Add “with mlflow.start_run():” before your training code

3) Start the tracking

Track parameters using:

    mlflow.log_param('testparameter', testparam)

Track metrics (can change every epoch):

    mlflow.log_metric('testparameter', testparam, step=epoch)

Track artefacts (figures, codes, etc. need to be saved before logging) using:

    mlflow.log_artifact('/tmp/test_fig.png')