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MLflow Projects—Making Code Reproducible

Having found a way to track code versions and parameters, we now want to go one step further and enable others to use our code on different machines or even run the code at scale on another platform, thus, making our results reproducible. This usually means that the whole environment (e.g., library dependencies) needs to be captured.

MLflow Projects are a standard format for packaging reusable data science code. Each project is simply a directory with code or a Git repository, and uses a descriptor file or simply convention to specify its dependencies and how to run the code.

A broad documentation on how to set up MLflow Projects can be found on MLflow's own documentation. Here, again, a shortened overview is given. Additionally, a template folder provides basic files to create an MLflow project. Hands-on tasks will be given afterwards.

Overview

How to package your code

At the core, MLflow Projects are just a convention for organizing and describing your code to let other data scientists (or automated tools) run it. Each project is simply a directory of files, or a Git repository, containing your code. MLflow can run some projects based on a convention for placing files in this directory (e.g., a conda.yaml file is treated as a Conda environment), but you can describe your project in more detail by adding a MLproject file, which is a YAML formatted text file. Each project can specify several properties:

  • Name: A human-readable name for the project.
  • Entry Points: Commands that can be run within the project, and information about their parameters. Most projects contain at least one entry point that you want other users to call. Some projects can also contain more than one entry point: e.g., you might have a single Git repository containing multiple featurization algorithms. You can also call any .py or .sh file in the project as an entry point. If you list your entry points in an MLproject file, however, you can also specify parameters for them, including data types and default values.
  • Environment: The software environment that should be used to execute project entry points. This includes all library dependencies required by the project code.

See the files in the template folder for more details or consult MLflow's documentation.

Commands

You can run any project from a Git URI or from a local directory using the mlflow run CLI tool, or the mlflow.projects.run() Python API. For more information on how to call the CLI command, consult

mlflow run --help

Attention, Windows users: As of the moment of writing (using MLflow 1.5.0), there is a bug running mlflow run in the Windows cmd CLI. The problem occurs when the automatically created conda environment is activated. An error message occurs, telling that the CLI is not properly configured to execeute conda activate although conda init cmd.exe has already been successfully executed. To circumvent this, you must change one line in MLflow's code. Open <MLFLOWVENV>\Lib\site-packages\mlflow\projects\\__init__.py and find the _run_entry_point function (ll. 494ff. in MLflow 1.6.0). Replace the line

process = subprocess.Popen(command, close_fds=True, cwd=work_dir, env=env)

with

process = subprocess.Popen(["cmd", "/c", command], close_fds=True, cwd=work_dir, env=env)

Now, you are good to go.

Tasks

  1. Take a look at the "Hello World" Project folder and its specific implementation. Run the project with the local folder as well as with the remote GitHub folder.

    set MLFLOW_TRACKING_URI=http://localhost:5000
mlflow_sklearn\Scripts\activate
mlflow run 20_projects\210_hello_world -P alpha=.01 -P run_origin=LocalRun -P log_artifact=True
mlflow run https://github.com/MynherVanKoek/AMLD_2020_MLflow.git#20_projects/210_hello_world -P alpha=.01 -P run_origin=GitRun -P log_artifact=True

    Again, Linux or Mac Users need to activate their virtual environment by using . mlflow_sklearn/bin/activate. For conda environments use conda activate mlflow_sklearn.

  2. Complete the Logistic Regression and Wine Classification folders and run them as well. You can also use their respective solution folders to them as remote GitHub projects.