We recommend using pyenv to manage your python versions. Once you have pyenv installed, you can create a new environment by running:
pyenv install 3.9.13
To activate the environment, run:
pyenv shell 3.9.13
Or, set as default by running:
pyenv global 3.9.13
Fork, clone and cd into the MLRun repository directory
git clone [email protected]:<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]'
We use black as our formatter. Format your code prior opening PR by running:
make fmt
-
Lint
make lint
-
Unit tests
make test-dockerized
-
Integration tests
make test-integration-dockerized
-
System tests - see dedicated section below
-
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
orbug
in the title of the PR, so that it will be added to theBugs & Fixes
section in the release notes. - Use imperative verbs when describing the changes made in the PR. For example, instead of writing
Adding endpoint to list runs
, writeAdd endpoint to list runs
. - Start with a verb after the
[<scope>]
prefix, e.g.[API] Add endpoint to list runs
.
- Begin the title of the PR with
-
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
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.
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).
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.
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.
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
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
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 3.9.13
python -m venv venv
pyenv shell 3.7.12
python -m venv venv37