![]() Try out this article on how to deploy a Jupyter notebook as a component in a Kubeflow pipeline: If you haven’t do so already, please read and walk through Part 1 of how to create and deploy a Kubeflow ML pipeline using Docker images. In my GitHub repo, creating and deploying the pipeline is shown in launcher.ipynb. As the pipeline runs, logs get streamed to the pipelines log, and will show up in Stackdriver:Īs the pipeline executes, the notebook cells’ outputs get streamed to Stackdriver and giving it the input and output notebooks and params. Nothing fancy - I’m creating a container, telling it to use my image that has TensorFlow, papermill, etc. But just to show you how it can be done, this is how you would create a pipeline that executes only this notebook: import kfp.components as comp import kfp.dsl as dsl # a single-op pipeline that runs the flights pipeline on the pod name='FlightsPipeline', description='Trains, deploys flights model' ) def flights_pipeline( inputnb=dsl.PipelineParam('inputnb'), outputnb=dsl.PipelineParam('outputnb'), params=dsl.PipelineParam('params') ): notebookop = dsl.ContainerOp( name='flightsmodel', image='gcr.io/cloud-training-demos/ submitnotebook:latest', arguments= ) The point of running the notebook as one step of the pipeline is so that it can be orchestrated and reused in other pipelines. Launch the notebook component as part of a pipeline That’s it! When this Docker image is run, it will execute the supplied notebook and copy the output notebook (with plots plotted, models trained, etc.) to GCS.BUCKET: cloud-training-demos-ml PROJECT: cloud-training-demos DEVELOP_MODE: False But params.yaml? What’s params.yaml? Those are the configurable parameters to the notebook.Essentially, the script copies the notebook to be run from Google Cloud Storage to the Kubeflow pod, runs the notebook with papermill and copies the resulting output back to Google Cloud Storage.Gsutil cp $IN_NB_GCS input.ipynb gsutil cp $PARAMS_GCS params.yaml papermill input.ipynb output.ipynb -f params.yaml -log-output gsutil cp output.ipynb $OUT_NB_GCS The entry point to the Docker image is run_notebook.sh which uses papermill to execute the notebook:.So my Dockerfile captures all those dependencies in the DockerfileįROM google/cloud-sdk:latest RUN apt-get update -y & apt-get install -no-install-recommends -y -q ca-certificates python3-dev python3-setuptools python3-pip RUN python3 -m pip install tensorflow=1.10 jupyter papermill COPY run_notebook.sh. My notebook uses Python3, gcloud and tensorflow. To execute a notebook, I will use the Python package papermill. I then build a Docker image that is capable of executing my notebook.Because I want you to be able to change them easily, I also make the PROJECT (to be billed) and the BUCKET (to store outputs) as parameters. In develop mode, I will read small datasets in not-develop-mode, I’ll train on the full dataset. ![]() In particular, I set up a variable called DEVELOP_MODE. In this cell, I define any variables that I will want to re-execute the notebook with. I have a cell at the top of my notebook whose tag is “parameters”.In order to deploy the flights_model notebook as a component: Can I execute this as a component as part of a Kubeflow pipeline? Recall from Part 1 that all it takes for something to be a component is for it to be a self-contained container that takes a few parameters and writes outputs to files, either on the Kubeflow cluster or on Cloud Storage. So, I have a full-fledged notebook that does some ML workflow. To get back, for each instance the probability that the flight will be late. One is that I developed this notebook mostly in Eager mode, for easy debugging: if EAGER_MODE: dataset = load_dataset(TRAIN_DATA_PATTERN) for n, data in enumerate(dataset): numpy_data = The actual TensorFlow code (See full notebook here: flights_model.ipynb) isn’t important, but I want you to notice a few things. Predicting flight delays using TensorFlow Switch back to the Jupyter notebooks listing and navigate to data-science-on-gcp/updates/cloudml and open up flights_model.ipynb. Then, open up a Terminal window and git clone my repo: git clone
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