History

Alexey Volkov bd0f4d23b9 chore(backend): Only compiling the preloaded samples. Fixes #4117 (#4118 ) * Backend - Only compiling the preloaded samples Fixes https://github.com/kubeflow/pipelines/issues/4117 * Fixed the paths * Removed -o pipefail for now since sh does not support it * Fixed the quotes * Removed the __future__ imports Python 2 is no longer supported. The annotations cause compilation problems: ``` File "/samples/core/iris/iris.py", line 18 from __future__ import absolute_import ^ SyntaxError: from __future__ imports must occur at the beginning of the file ```	2020-07-06 20:03:57 -07:00
..
README.md	TFX Iris sample (#3119 )	2020-03-13 17:34:34 -07:00
iris.py	chore(backend): Only compiling the preloaded samples. Fixes #4117 (#4118 )	2020-07-06 20:03:57 -07:00

chore(backend): Only compiling the preloaded samples. Fixes #4117 (#4118 )

* Backend - Only compiling the preloaded samples

Fixes https://github.com/kubeflow/pipelines/issues/4117

* Fixed the paths

* Removed -o pipefail for now since sh does not support it

* Fixed the quotes

* Removed the __future__ imports

Python 2 is no longer supported.
The annotations cause compilation problems:
```
  File "/samples/core/iris/iris.py", line 18
    from __future__ import absolute_import
    ^
SyntaxError: from __future__ imports must occur at the beginning of the file
```

2020-07-06 20:03:57 -07:00

README.md

TFX Iris sample (#3119 )

2020-03-13 17:34:34 -07:00

iris.py

chore(backend): Only compiling the preloaded samples. Fixes #4117 (#4118 )

2020-07-06 20:03:57 -07:00

README.md

Overview

Tensorflow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. Kubeflow Pipelines can be used as the orchestrator supporting the execution of a TFX pipeline.

This directory contains a sample that demonstrate how to author a ML pipeline to solve the famous iris flower classification problem in TFX and run it on a KFP deployment. Specifically it highlights the following functionalities:

Support of Keras API;
Use TFMA for model validation;
Warm-start training by Resolver.

Compilation

In order to successfully compile the Python sample, it is recommended to use tfx>=0.21.2.

Permission

⚠️ If you are using full-scope or workload identity enabled cluster in hosted pipeline beta version, DO NOT follow this section. However you'll still need to enable corresponding GCP API.

This pipeline requires Google Cloud Storage permission to run. If KFP was deployed through K8S marketplace, please follow instructions in the guideline to make sure the service account has storage.admin role. If KFP was deployed through standalone deployment please refer to Authenticating Pipelines to GCP to provide storage.admin permission.