Kubeflow – Samples and Tutorials

Docs: Using the Kubeflow Pipelines SDK

Mon, 01 Jan 0001 00:00:00 +0000

This guide provides examples that demonstrate how to use the Kubeflow Pipelines SDK.

Before you start

To follow the examples in this guide, you must have Kubeflow Pipelines SDK version 0.2.5 or higher installed. Use the following instructions to install the Kubeflow Pipelines SDK and check the SDK version.

Install the Kubeflow Pipelines SDK
Run the following command to check the version of the SDK
```
pip list | grep kfp
```
The response should be something like this:
```
kfp                      0.2.5
kfp-server-api           0.2.5
```

Examples

Use the following examples to learn more about the Kubeflow Pipelines SDK.

Creating a pipeline and a pipeline version using the SDK

The following example demonstrates how to use the Kubeflow Pipelines SDK to create a pipeline and a pipeline version.

In this example, you:

Use kfp.Client to create a pipeline from a local file. When the pipeline is created, a default pipeline version is automatically created.
Use kfp.Client to add a pipeline version to the pipeline that was created in the previous step.

import kfp
import os

host = <host>
pipeline_file_path = <path to pipeline file>
pipeline_name = <pipeline name>
pipeline_version_file_path = <path to pipeline version file>
pipeline_version_name = <pipeline version name>

client = kfp.Client(host)
pipeline_file = os.path.join(pipeline_file_path)
pipeline = client.pipeline_uploads.upload_pipeline(pipeline_file, name=pipeline_name)
pipeline_version_file = os.path.join(pipeline_version_file_path)
pipeline_version = client.pipeline_uploads.upload_pipeline_version(pipeline_version_file,
                                                                   name=pipeline_version_name,
                                                                   pipelineid=pipeline.id)

host: Your Kubeflow Pipelines cluster’s host name.
path to pipeline file: The path to the directory where your pipeline YAML is stored.
pipeline name: Your pipeline’s file name.
path to pipeline version file: The path to the directory where the new version of your pipeline YAML is stored.
pipeline version name: Your pipeline version’s file name.

Note: Pipeline names need to be unique across your Kubeflow Pipelines cluster. Pipeline version names need to be unique within each pipeline.

Adding a version to an existing pipeline using the SDK

To add a pipeline version for an existing pipeline, you must find the pipeline’s ID and use it with the upload_pipeline_version method. To find a pipeline’s ID:

Open the Kubeflow Pipelines UI. A list of your pipelines appears.
Click the name of your pipeline. The pipeline details page appears.
The pipeline ID is listed in the summary card, as shown below.

Creating a run using a pipeline version

Examine the run_service_api.ipynb notebook to learn more about creating a run using a pipeline version.

Docs: Experiment with the Kubeflow Pipelines API

Mon, 01 Jan 0001 00:00:00 +0000

This tutorial demonstrates how to use the Kubeflow Pipelines API to build, run, and manage pipelines. This guide is recommended for users who would like to learn how to manage Kubeflow Pipelines using the REST API.

Before you start

This tutorial assumes that you have access to the ml-pipeline service. If Kubeflow is not configured to use an identity provider, use port-forwarding to directly access the service.

SVC_PORT=$(kubectl -n kubeflow get svc/ml-pipeline -o json | jq ".spec.ports[0].port")
kubectl port-forward -n kubeflow svc/ml-pipeline ${SVC_PORT}:8888

This tutorial assumes that the service is accessible on localhost.

You also need to install jq, and the Kubeflow Pipelines SDK.

Building and running a pipeline

Follow this guide to download, compile, and run the sequential.py sample pipeline. To learn how to compile and run pipelines using the Kubeflow Pipelines SDK or a Jupyter notebook, follow the experimenting with Kubeflow Pipelines samples tutorial.

PIPELINE_URL=https://raw.githubusercontent.com/kubeflow/pipelines/master/samples/core/sequential/sequential.py
PIPELINE_FILE=${PIPELINE_URL##*/}
PIPELINE_NAME=${PIPELINE_FILE%.*}

wget -O ${PIPELINE_FILE} ${PIPELINE_URL}
dsl-compile --py ${PIPELINE_FILE} --output ${PIPELINE_NAME}.tar.gz

After running the commands above, you should get two files in your current directory: sequential.py and sequential.tar.gz. Run the following command to deploy the generated .tar.gz file as you would do using the Kubeflow Pipelines UI, but this time using the REST API.

SVC=localhost:8888
PIPELINE_ID=$(curl -F "uploadfile=@${PIPELINE_NAME}.tar.gz" ${SVC}/apis/v1beta1/pipelines/upload | jq -r .id)

If the operation was successful, you should see the pipeline in the central dashboard. You can also get the details using the PIPELINE_ID with the following API call.

curl ${SVC}/apis/v1beta1/pipelines/${PIPELINE_ID} | jq

The response should be similar to the following one:

{
  "id": "d30d28d7-0bfc-4f0c-8a57-6844a8ec9742",
  "created_at": "2020-02-20T16:15:02Z",
  "name": "sequential.tar.gz",
  "parameters": [
    {
      "name": "url",
      "value": "gs://ml-pipeline-playground/shakespeare1.txt"
    }
  ],
  "default_version": {
    "id": "d30d28d7-0bfc-4f0c-8a57-6844a8ec9742",
    "name": "sequential.tar.gz",
    "created_at": "2020-02-20T16:15:02Z",
    "parameters": [
      {
        "name": "url",
        "value": "gs://ml-pipeline-playground/shakespeare1.txt"
      }
    ],
    "resource_references": [
      {
        "key": {
          "type": "PIPELINE",
          "id": "d30d28d7-0bfc-4f0c-8a57-6844a8ec9742"
        },
        "relationship": "OWNER"
      }
    ]
  }
}

Finally, use the PIPELINE_ID to trigger a run of your pipeline.

RUN_ID=$((
curl -H "Content-Type: application/json" -X POST ${SVC}/apis/v1beta1/runs \
-d @- << EOF
{
   "name":"${PIPELINE_NAME}_run",
   "pipeline_spec":{
      "pipeline_id":"${PIPELINE_ID}"
   }
}
EOF
) | jq -r .run.id)

Run the following command occasionally to see how the status of your run changes. After a while, the status of your pipeline should change to Succeeded.

curl ${SVC}/apis/v1beta1/runs/${RUN_ID} | jq

The response should be similar to the following one:

{
  "run": {
    "id": "4ff0debd-d6d7-4681-8593-21ec002e6e0c",
    "name": "sequential_run",
    "pipeline_spec": {
      "pipeline_id": "d30d28d7-0bfc-4f0c-8a57-6844a8ec9742",
      "pipeline_name": "sequential.tar.gz",
      "workflow_manifest": "{...}"
    },
    "resource_references": [
      {
        "key": {
          "type": "EXPERIMENT",
          "id": "27af7eee-ce0a-44ba-a44d-07142abfc83c"
        },
        "name": "Default",
        "relationship": "OWNER"
      }
    ],
    "created_at": "2020-02-20T16:18:58Z",
    "scheduled_at": "1970-01-01T00:00:00Z",
    "finished_at": "1970-01-01T00:00:00Z",
    "status": "Succeeded"
  },
  "pipeline_runtime": {
    "workflow_manifest": "{...}"
  }
}

Read Kubeflow Pipelines API Reference to learn more about how to use the API.

Docs: Experiment with the Pipelines Samples

Mon, 01 Jan 0001 00:00:00 +0000

You can learn how to build and deploy pipelines by running the samples provided in the Kubeflow Pipelines repository or by walking through a Jupyter notebook that describes the process.

Compiling the samples on the command line

This section shows you how to compile the Kubeflow Pipelines samples and deploy them using the Kubeflow Pipelines UI.

Before you start

Set up your environment:

Clone or download the Kubeflow Pipelines samples.
Install the Kubeflow Pipelines SDK.
Activate your Python 3 environment if you haven’t done so already:
```
source activate <YOUR-PYTHON-ENVIRONMENT-NAME>
```
For example:
```
source activate mlpipeline
```

Choose and compile a pipeline

Examine the pipeline samples that you downloaded and choose one to work with. The sequential.py sample pipeline: is a good one to start with.

Each pipeline is defined as a Python program. Before you can submit a pipeline to the Kubeflow Pipelines service, you must compile the pipeline to an intermediate representation. The intermediate representation takes the form of a YAML file compressed into a .tar.gz file.

Use the dsl-compile command to compile the pipeline that you chose:

dsl-compile --py [path/to/python/file] --output [path/to/output/tar.gz]

For example, to compile the sequential.py sample pipeline:

export DIR=[YOUR PIPELINES REPO DIRECTORY]/samples/core/sequential
dsl-compile --py ${DIR}/sequential.py --output ${DIR}/sequential.tar.gz

Deploy the pipeline

Upload the generated .tar.gz file through the Kubeflow Pipelines UI. See the guide to getting started with the UI.

Building a pipeline in a Jupyter notebook

You can choose to build your pipeline in a Jupyter notebook. The sample notebooks walk you through the process.

It’s easiest to use the Jupyter services that are installed in the same cluster as the Kubeflow Pipelines system.

Note: The notebook samples don’t work on Jupyter notebooks outside the same cluster, because the Python library communicates with the Kubeflow Pipelines system through in-cluster service names.

Follow these steps to start a notebook:

Deploy Kubeflow:
- Follow the GCP deployment guide, including the step to deploy Kubeflow using the Kubeflow deployment UI.
- When Kubeflow is running, access the Kubeflow UI at a URL of the form https://<deployment-name>.endpoints.<project>.cloud.goog/.
Follow the Kubeflow notebooks setup guide to create a Jupyter notebook server and open the Jupyter UI.
Download the sample notebooks from https://github.com/kubeflow/pipelines/tree/master/samples/core.
Upload these notebooks from the Jupyter UI: In Jupyter, go to the tree view and find the upload button in the top right-hand area of the screen.
Open one of the uploaded notebooks.
Make sure the notebook kernel is set to Python 3. The Python version is at the top right-hand corner in the Jupyter notebook view.
Follow the instructions in the notebook.

The following notebooks are available:

KubeFlow pipeline using TFX OSS components: This notebook demonstrates how to build a machine learning pipeline based on TensorFlow Extended (TFX) components. The pipeline includes a TFDV step to infer the schema, a TFT preprocessor, a TensorFlow trainer, a TFMA analyzer, and a model deployer which deploys the trained model to tf-serving in the same cluster. The notebook also demonstrates how to build a component based on Python 3 inside the notebook, including how to build a Docker container.
Lightweight Python components: This notebook demonstrates how to build simple Python components based on Python 3 and use them in a pipeline with fast iterations. If you use this technique, you don’t need to build a Docker container when you build a component. Note that the container image may not be self contained because the source code is not built into the container.

Next steps

Learn the various ways to use the Kubeflow Pipelines SDK.
See how to build your own pipeline components.
Read more about building lightweight components.

Docs: Run a Cloud-specific Pipelines Tutorial

Mon, 01 Jan 0001 00:00:00 +0000

Opportunity to add cloud tutorials

Invitation: Create a cloud-specific tutorial and link it here. See the guide to the Kubeflow docs.

Pipelines End-to-end on Azure: An end-to-end tutorial for Kubeflow Pipelines on Microsoft Azure.
Pipelines on Google Cloud Platform : This GCP tutorial walks through a Kubeflow Pipelines example that shows training a Tensor2Tensor model for GitHub issue summarization, both via the Pipelines Dashboard UI, and from a Jupyter notebook