Chapter 04. Kubeflow Pipelines

$ cd ~
$ source kfvenv/bin/activate

$ cd ~/tmp/Kubeflow-for-Machine-Learning-From-Lab-to-Production/ch04/

$ vi RecommenderPipeline.py

// Наверное
# exp = client.create_experiment(name='mdupdate')
exp = client.get_experiment(experiment_name='mdupdate')

$ pip install numpy kubernetes kfp
$ dsl-compile --py LightweightPipeline.py --output LightweightPipeline.yaml

$ dsl-compile --py RecommenderPipeline.py --output RecommenderPipeline.yaml

$ wget https://github.com/kubeflow/pipelines/archive/0.2.5.tar.gz
$ tar -xvf 0.2.5.tar.gz

$ dsl-compile --py ConditionalPipeline.py --output ConditionalPipeline.yaml

$ argo submit LightweightPipeline.yaml -n kubeflow -p deploy-model=true
$ argo submit RecommenderPipeline.yaml -n kubeflow -p deploy-model=true
$ argo submit ConditionalPipeline.yaml -n kubeflow -p deploy-model=true

// OK!
RUN -> Pipeline -> LightweightPipeline -> Запускается контейнер: calculation-pipeline

// FAIL! Error reading file recommender/directory.txt from bucket data
RUN -> Pipeline -> RecommenderPipeline -> Запускается recommender-model-update

// OK!
RUN -> Pipeline -> LightweightPipeline -> Запускается conditional-execution-pipeline

$ cd data-extraction/python-notebook
$ dsl-compile --py MailingListDataPrep.py --output MailingListDataPrep.yaml