Contributing To MLRun

Creating a development environment

If you are working with an ARM64 machine, please see Developing with ARM64 machines.

We recommend using pyenv to manage your python versions. Once you have pyenv installed, you can create a new environment by running:

pyenv install 3.9

To activate the environment, run:

pyenv shell 3.9

Or, set as default by running:

pyenv global 3.9

Fork, clone and cd into the MLRun repository directory

git clone git@github.com:<your username>/mlrun.git
cd mlrun

Set up a virtualenv (we recommend using venv)

python -m venv venv
source venv/bin/activate

Install MLRun, dependencies and dev dependencies

make install-requirements
pip install -e '.[complete]'

Developing with ARM64 machines

Some mlrun dependencies are not yet available for ARM64 machines via pypi, so we need to work with conda to get the packages compiled for ARM64 platform.
Install Anaconda from here and then follow the steps below:

Fork, clone and cd into the MLRun repository directory

git clone git@github.com:<your username>/mlrun.git
cd mlrun

Create a conda environment and activate it

conda create -n mlrun python=3.9
conda activate mlrun

Then, install the dependencies

make install-conda-requirements

Run some unit tests to make sure everything works:

python -m pytest ./tests/projects

If you encounter any error with 'charset_normalizer' for example:

AttributeError: partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)

Run:

pip install --force-reinstall charset-normalizer

Finally, install mlrun

pip install -e '.[complete]'

Formatting and Linting

We use ruff as our formatter and linter. Format your code prior opening PR by running:

make fmt

Testing

Lint
```
make lint
```
Unit tests
```
make test-dockerized
```
Integration tests
```
make test-integration-dockerized
```
System tests - see dedicated section below

Pull requests

Title
- Begin the title of the PR with [<scope>] , with the first letter of the component name in uppercase, e.g [API] Add endpoint to list runs.
- If the PR is addressing a bug, include the keywords fix or bug in the title of the PR, so that it will be added to the Bugs & Fixes section in the release notes. Additionally, the PR title should reflect how the fix was done and not how it was fixed. For example if there was a race condition where an artifact got deleted and created at the same time, instead of writing "Fixed artifact locking mechanism", write "Fixed artifact creation/deletion race condition".
- Use imperative verbs when describing the changes made in the PR. For example, instead of writing Adding endpoint to list runs, write Add endpoint to list runs.
- Start with a verb after the [<scope>] prefix, e.g. [API] Add endpoint to list runs.
Description - It's much easier to review when there is a detailed description of the changes, and especially the why-s, please put effort in writing good description
Tests - we care a lot about tests! if your PR will include good test coverage higher chances it will be merged fast

System Tests

As we support additional enterprise features while running MLRun in an Iguazio system, some system tests can only run on an Iguazio system. To support this, we have two types of system tests. Using @pytest.mark.enterprise markers, we can distinguish between tests that can run on a MLRun Community Edition instance and tests that requires and can only run on a full Iguazio system. Any system test which isn't marked with the @pytest.mark.enterprise marker can run on MLRun Community Edition which incidentally can also be installed locally on a developer machine.

In the tests/system/ directory exist test suites to run against a running system, in order to test full MLRun flows.

Setting Up an MLRun Community Edition Instance for System Tests

You can follow the Install MLRun on Kubernetes guide to install an instance of MLRun Community Edition on your local machine. Notice the mentioned prerequisites and make sure you have some kubernetes cluster running locally. You can use minikube for this purpose (however this will require an extra step, see below).

Setting up Test Environment

To run the system tests, you need to set up the tests/system/env.yml file. For running the open source system tests, the only requirement is to set the MLRUN_DBPATH environment variable to the url of the mlrun api service which is installed as part of the MLRun Community Edition installation. Once the installation is completed, it outputs a list of urls which you can use to access the various services. Similar to:

NOTES:
You're up and running !

1. Jupyter UI is available at:
  http://127.0.0.1:30040
...
4. MLRun API is exposed externally at:
  http://127.0.0.1:30070
...
Happy MLOPSing !!! :]

Notice the "MLRun API is exposed externally at: http://127.0.0.1:30070" line. This is the url you need to set in the env.yml file, as the MLRUN_DBPATH value..

If running via minikube, you will first need to run

minikube -n mlrun service mlrun-api

Which will tunnel the mlrun api service to your local machine. You can then use the url that is outputted by this command to set the MLRUN_DBPATH environment variable.

Adding System Tests

To add new system tests, all that is required is to create a test suite class which inherits the TestMLRunSystem class from tests.system.base. In addition, a special skip annotation must be added to the suite, so it won't run if the env.yml isn't filled. If the test can only run on a full Iguazio system and not on an MLRun CE instance, add the enterprise marker under the skip annotation or on the test method itself. If the enterprise marker is added to a specific test method, the skip annotation must be added above it in addition to the annotation over the test suite. This is because enterprise tests and open source tests require different env vars to be set in the env.yml.

For example:

import pytest
from tests.system.base import TestMLRunSystem

@TestMLRunSystem.skip_test_if_env_not_configured
@pytest.mark.enterprise
class TestSomeFunctionality(TestMLRunSystem):
    def test_the_functionality(self):
        pass

Example of a suite with two tests, one of them meant for enterprise only

import pytest
from tests.system.base import TestMLRunSystem

@TestMLRunSystem.skip_test_if_env_not_configured
class TestSomeFunctionality(TestMLRunSystem):

    def test_open_source_features(self):
        pass

    @TestMLRunSystem.skip_test_if_env_not_configured
    @pytest.mark.enterprise
    def test_enterprise_features(self):
        pass

If some setup or teardown is required for the tests in the suite, add these following functions to the suite:

from tests.system.base import TestMLRunSystem

@TestMLRunSystem.skip_test_if_env_not_configured
class TestSomeFunctionality(TestMLRunSystem):
    
    def custom_setup(self):
        pass
    
    def custom_teardown(self):
        pass
    
    def test_the_functionality(self):
        pass

From here, just use the MLRun SDK within the setup/teardown functions and the tests themselves with regular pytest functionality. The MLRun SDK will work against the live system you configured, and you can write the tests as you would any other pytest test.

You're Done!

All that's left now is to run whichever open source system tests you want to run. You can run them all by running the command

make test-system-open-source

Checking system test regression on new code

Currently, this can only be done by one of the maintainers, the process is:

Push your changes to a branch in the upstream repo
Go to the build action and trigger it for the branch (leave all options default)
Go to the system test action and trigger it for the branch, change "Take tested code from action REF" to true

Migrating to Python 3.9

MLRun moved to Python 3.9 from 1.3.0.
If you are working on MLRun 1.2.x or earlier, you will need to switch between python 3.9 and python 3.7 interpreters. To work with multiple python interpreters, we recommend using pyenv (see Creating a development environment). Once you have pyenv installed, create multiple venv for each Python version, so when you switch between them, you will have the correct dependencies installed. You can manage and switch venvs through PyCharm project settings.

e.g.:

pyenv shell 3.9
pyenv virtualenv mlrun

pyenv shell 3.7
pyenv virtualenv mlrun37

Python Code Conventions:

Use snake_case for parameters, functions and module names.
Use CamelCase for Class names.
Style conventions (handled by linter) can be found here: https://pycodestyle.pycqa.org/en/latest/intro.html#error-codes
Functions must be kept short, read like a story and break into smaller functions when needed.
Use meaningful names and avoid abbreviations for variables, functions and modules to enhance readability.
Avoid using hardcoded values, use Enum values, env variables or configuration files instead.
Use typing hints for complex data structures.
Use tmp_path fixture for temporary files/directories in tests.
When using a function in the same file, it should be declared below the calling function as it makes it easier to understand the functionality.
Private methods should be declared below the public ones.
When dealing with numerical values representing sizes or time related variables, add a comment to specify the unit (e.g., KB, MB, seconds, hours) for clarity, or alternatively, use variable names like “size_in_KB” for explicit unit indication.
After updating an existing code, ensure that the old documentation is synced with the newly added code or needs to be updated accordingly.
When importing from local code, NEVER use: from X import Y instead use: import X or import X as Y. For external packages, it's acceptable to use from X import Y since they won't try to import back to our code.
Docstring format, for all public API-level functions: Format: Use triple quotes (""" """) for docstrings. Description: Provide a brief and informative description of the function's purpose. Parameters: List all parameters with their data types and a brief description. Use the :param tag for each parameter. Return Value: If the function returns a value, describe it using the :return tag. Example:

def function_name(parameter1, parameter2):
	"""
	Brief description of the function's purpose.
	:param parameter1: Description of parameter1.
	:param parameter2: Description of parameter2.
	:return: Description of the return value (if applicable).
	"""
	# Function implementation

When calling functions with multiple parameters, prefer using keyword arguments to improve readability and clarity.
Logging: use structured variable instead of f-strings, for example: logger.debug("Message", var1=var1, ...), and try to avoid logging large objects which are hard to decipher.
Use f-strings for string formatting instead of the old .format(...) except when dealing with template strings.

MLRun Code Conventions:

When converting an error object to a string representation, instead of using: str(error) use: mlrun.errors.err_to_str(error)
Use mlrun.mlconf Instead of mlrun.config.config.
When deprecating a parameter/method/class we keep backwards compatibility for 2 minor versions. For example if we deprecated a parameter in 1.6.0, it will be removed in 1.8.0. Always specify what should be used instead. If there is nothing to be used instead, specify why.

Deprecating a parameter: Check if the parameter is given and output a FutureWarning and add a TODO with when this should be removed to help developers keep track. for example:

if uid:
	warnings.warn(
		"'uid' is deprecated in 1.6.0 and will be removed in 1.8.0, use 'tree' instead.",
		# TODO: Remove this in 1.8.0
		FutureWarning,
	)

Deprecating a method: Use 'deprecated'

# TODO: remove in 1.6.0
@deprecated(
	version="1.4.0",
	reason="'verify_base_image' will be removed in 1.6.0, use 'prepare_image_for_deploy' instead",
	category=FutureWarning,
)
def verify_base_image(self):

Deprecating a class:

# TODO: Remove in 1.7.0
@deprecated(
	version="1.5.0",
	reason="v1alpha1 mpi will be removed in 1.7.0, use v1 instead",
	category=FutureWarning,
)
class MpiRuntimeV1Alpha1(AbstractMPIJobRuntime):

Minimize imports and avoid unnecessary dependencies in client code.
Scale performance: be caution when executing large queries in order to prevent overloading the database.

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CONTRIBUTING.md

CONTRIBUTING.md

Contributing To MLRun

Creating a development environment

Developing with ARM64 machines

Formatting and Linting

Testing

Pull requests

System Tests

Setting Up an MLRun Community Edition Instance for System Tests

Setting up Test Environment

Adding System Tests

You're Done!

Checking system test regression on new code

Migrating to Python 3.9

Python Code Conventions:

MLRun Code Conventions:

Files

CONTRIBUTING.md

Latest commit

History

CONTRIBUTING.md

File metadata and controls

Contributing To MLRun

Creating a development environment

Developing with ARM64 machines

Formatting and Linting

Testing

Pull requests

System Tests

Setting Up an MLRun Community Edition Instance for System Tests

Setting up Test Environment

Adding System Tests

You're Done!

Checking system test regression on new code

Migrating to Python 3.9

Python Code Conventions:

MLRun Code Conventions: