pipelinelocalrun 0.2.0

Description:

pipelinelocalrun 0.2.0

AML Pipeline Local Run Guide
This repository contains sdk code to run aml pipeline locally.
So far there are 2 job running modes supported in pipeline local run:

Native: this mode means run the job in native process
Container: this mode the local pipeline executor will help building the container based on the environment defined in pipeline job component.

limitations:

Only support CommandJob (SweepJob and DistributedJob are not supported)
Pure local run with local metrics/UI support (need to run local server container if want to see ui)

project structure

piprunengine: main pkg for pipeline local executor
tests: unit test to run all sample pipelines (defined in notebookxx)
notebookxx: unit test pipeline definition and related resources.

prerequisites

Docker
azure ml devplat-v2 sdk
python version > 3.7

How to run it
1.1 clone this repo to local
1.2 create a new python env with conda
conda create -n <local_run_env> python=3.9

1.3 install the local run sdk
find the latest wheel in release folder and install it in the new created env
pip install pipelinelocalrun==0.1.9

1.4 install jupyter if want to run in notebook
pip install jupyter

1.5 start local web server if want to try local ui & mlflow (optional)
docker run -e AML_LOCAL_RUN_DB_PATH="/metadata/localrun.db" -e LOCALUI_START=true -p 127.0.0.1:4999:80/tcp -p 127.0.0.1:8080:8080/tcp -p 127.0.0.1:3001:3001/tcp -p 127.0.0.1:5000:5000/tcp --mount type=bind,source='<current-user-home-path>\.azureml\piprun',target=/metadata <image_name:tag>

<current-user-home-path>:

for windows: C:\Users\<username>
for linux: /home/<username>

<image_name:tag> is the image built in step 2.4
1.6 how to use in code/notebook


import the local run func from the install pkg
from piprunengine import run



following the normal steps in the notebook to build your pipeline(you can ignore all steps which need interaction with aml workspace)


start pipeline local run
output_root_dir = "./local-run-output/notebook_1a_native"
# set pipeline name
pipeline.name="test-pipeline"
run(output_root_dir=output_root_dir, job=pipeline, experiment_name="test")

note: by default it runs in CONTAINER mode and it will build the curated container locally and run in container mode.


1.7 try with notebook demo

notebook1a/pipeline_with_components_from_yaml.ipynb
notebook1b/pipeline_with_python_function_components.ipynb
notebook2c/nyc_taxi_data_regression.ipynb
notebook1d/pipeline_with_non_python_components.ipynb

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