Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way.
The platform provides the abstractions and declarative capabilities for data extraction & feature engineering followed by model training and serving. Liminal's goal is to operationalize the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validation, deployment and inference in production, freeing them from engineering and non-functional tasks, and allowing them to focus on machine learning code and artifacts.
Using simple YAML configuration, create your own schedule data pipelines (a sequence of tasks to perform), application servers, and more.
A simple getting stated guide for Liminal can be found here
Full documentation of Apache Liminal can be found here
High level architecture documentation can be found here
--- name: MyLiminalStack owner: Bosco Albert Baracus volumes: - volume: myvol1 local: path: /Users/me/myvol1 images: - image: my_python_task_img type: python source: write_inputs - image: my_parallelized_python_task_img source: write_outputs - image: my_server_image type: python_server source: myserver endpoints: - endpoint: /myendpoint1 module: my_server function: myendpoint1func pipelines: - pipeline: my_pipeline start_date: 1970-01-01 timeout_minutes: 45 schedule: 0 * 1 * * metrics: namespace: TestNamespace backends: [ 'cloudwatch' ] tasks: - task: my_python_task type: python description: static input task image: my_python_task_img env_vars: NUM_FILES: 10 NUM_SPLITS: 3 mounts: - mount: mymount volume: myvol1 path: /mnt/vol1 cmd: python -u write_inputs.py - task: my_parallelized_python_task type: python description: parallelized python task image: my_parallelized_python_task_img env_vars: FOO: BAR executors: 3 mounts: - mount: mymount volume: myvol1 path: /mnt/vol1 cmd: python -u write_inputs.py services: - service: my_python_server description: my python server image: my_server_image
pip install git+https://github.com/apache/incubator-liminal.git
echo 'export LIMINAL_HOME=</path/to/some/folder>' >> ~/.bash_profile && source ~/.bash_profile
This involves at minimum creating a single file called liminal.yml as in the example above.
If your pipeline requires custom python code to implement tasks, they should be organized like this
If your pipeline introduces imports of external packages which are not already a part of the liminal framework (i.e. you had to pip install them yourself), you need to also provide a requirements.txt in the root of your project.
When your pipeline code is ready, you can test it by running it locally on your machine.
And allocate it at least 3 CPUs (under “Resources” in the Docker preference UI).
If you want to execute your pipeline on a remote kubernetes cluster, make sure the cluster is configured using :
kubectl config set-context <your remote kubernetes cluster>
In the example pipeline above, you can see that tasks and services have an “image” field - such as “my_static_input_task_image”. This means that the task is executed inside a docker container, and the docker container is created from a docker image where various code and libraries are installed.
You can take a look at what the build process looks like, e.g. here
In order for the images to be available for your pipeline, you'll need to build them locally:
cd </path/to/your/liminal/code> liminal build
You'll see that a number of outputs indicating various docker images built.
cd </path/to/your/liminal/code> liminal create
cd </path/to/your/liminal/code> liminal deploy
Note: after upgrading liminal, it's recommended to issue the command
liminal deploy --clean
This will rebuild the airlfow docker containers from scratch with a fresh version of liminal, ensuring consistency.
liminal logs --follow/--tail Number of lines to show from the end of the log: liminal logs --tail=10 Follow log output: liminal logs --follow
Navigate to http://localhost:8080/admin
You should see your The pipeline is scheduled to run according to the
json schedule: 0 * 1 * * field in the .yml file you provided.
To manually activate your pipeline: Click your pipeline and then click “trigger DAG” Click “Graph view” You should see the steps in your pipeline getting executed in “real time” by clicking “Refresh” periodically.
More information on contributing can be found here
When doing local development and running Liminal unit-tests, make sure to set LIMINAL_STAND_ALONE_MODE=True