liminal 0.0.2.dev7
A package for authoring and deploying machine learning workflows
License: Apache License, Version 2.0
Requires Python: >=3.6

<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements.  See the NOTICE file
distributed with this work for additional information
regarding copyright ownership.  The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License.  You may obtain a copy of the License at

  http://www.apache.org/licenses/LICENSE-2.0

Unless required bgit y applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied.  See the License for the
specific language governing permissions and limitations
under the License.
-->

# Liminal

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.

# Basics

Using simple YAML configuration, create your own schedule data pipelines (a sequence of tasks to
perform), application servers,  and more.

## Example YAML config file
```yaml
name: MyPipeline
owner: Bosco Albert Baracus
pipelines:
  - pipeline: my_pipeline
    start_date: 1970-01-01
    timeout_minutes: 45
    schedule: 0 * 1 * *
    metrics:
     namespace: TestNamespace
     backends: [ 'cloudwatch' ]
    tasks:
      - task: my_static_input_task
        type: python
        description: static input task
        image: my_static_input_task_image
        source: helloworld
        env_vars:
          env1: "a"
          env2: "b"
        input_type: static
        input_path: '[ { "foo": "bar" }, { "foo": "baz" } ]'
        output_path: /output.json
        cmd: python -u hello_world.py
      - task: my_parallelized_static_input_task
        type: python
        description: parallelized static input task
        image: my_static_input_task_image
        env_vars:
          env1: "a"
          env2: "b"
        input_type: static
        input_path: '[ { "foo": "bar" }, { "foo": "baz" } ]'
        split_input: True
        executors: 2
        cmd: python -u helloworld.py
      - task: my_task_output_input_task
        type: python
        description: task with input from other task's output
        image: my_task_output_input_task_image
        source: helloworld
        env_vars:
          env1: "a"
          env2: "b"
        input_type: task
        input_path: my_static_input_task
        cmd: python -u hello_world.py
services:
  - service:
    name: my_python_server
    type: python_server
    description: my python server
    image: my_server_image
    source: myserver
    endpoints:
      - endpoint: /myendpoint1
        module: myserver.my_server
        function: myendpoint1func
```


# Installation
1. Install this package
```bash
   pip install liminal
```
2. Optional: set LIMINAL_HOME to path of your choice (if not set, will default to ~/liminal_home)
```bash
echo 'export LIMINAL_HOME=</path/to/some/folder>' >> ~/.bash_profile && source ~/.bash_profile
```

# Authoring pipelines

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](https://github.com/apache/incubator-liminal/tree/master/tests/runners/airflow/liminal)

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.

# Testing the pipeline locally

When your pipeline code is ready, you can test it by running it locally on your machine.

1. Ensure you have The Docker engine running locally, and enable a local Kubernetes cluster:
![Kubernetes configured](https://raw.githubusercontent.com/apache/incubator-liminal/master/images/k8s_running.png)

If you want to execute your pipeline on a remote kubernetes cluster, make sure the cluster is configured
using :
```bash
kubectl config set-context <your remote kubernetes cluster>
``` 
2. Build the docker images used by your pipeline.

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](https://github.com/apache/incubator-liminal/tree/master/liminal/build/image/python)

In order for the images to be available for your pipeline, you'll need to build them locally:

```bash
cd </path/to/your/liminal/code>
liminal build
```

You'll see that a number of outputs indicating various docker images built.

3. Deploy the pipeline:
```bash
cd </path/to/your/liminal/code> 
liminal deploy
```

4. Start the server
```bash
liminal start
```

5. Navigate to [http://localhost:8080/admin](http://localhost:8080/admin)

6. You should see your ![pipeline](https://raw.githubusercontent.com/apache/incubator-liminal/master/images/airflow.png)
The pipeline is scheduled to run according to the ```json schedule: 0 * 1 * *``` field in the .yml file you provided.

7. 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.

![Pipeline activation](https://raw.githubusercontent.com/apache/incubator-liminal/master/images/airflow_trigger.png)

### Running Tests (for contributors)
When doing local development and running Liminal unit-tests, make sure to set LIMINAL_STAND_ALONE_MODE=True